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-->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThis advanced guide walks through an end-to-end customer segmentation machine-learning use-case using Snowflake Feature Store and Model Registry.  By completing this guide, you will be able to go from ingesting raw data through to implementing a production inference data-pipeline with Snowflake ML to maintain customer segments.\u003C/p\u003E\n","\u003Cp\u003EThe primary focus in this guide is the Snowflake Feature Stores functionality and how it integrates within the broader ecosystem within Snowflake ML.\u003C/p\u003E\n","\u003Cp\u003EHere is a summary of what you will be able to learn in each step by following this Guide:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESetup Environment\u003C/strong\u003E: Use stages and tables to ingest and organize raw data from S3 into Snowflake tables.  Setup a scheduled process to simulate incremental data-ingest into Snowflake tables.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFeature Engineering\u003C/strong\u003E: Leverage Snowparks Python DataFrames to perform data cleansing, transformations such as group by, aggregate, pivot, and join to create features for machine learning.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFeature Store\u003C/strong\u003E: Use Snowflakes Feature Store to register and maintain feature-engineering pipelines and understand how to monitor them once operational.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMachine Learning\u003C/strong\u003E: Perform feature transformation and run ML Training in Snowflake using Snowpark ML. Register the trained ML model for inference from the Snowflake Model Registry\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOperationalise a Model\u003C/strong\u003E: Implementing a production inference data-pipeline to maintain customer segments as underlying customer behaviors change in source data.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe diagram below provides an overview of what we will be building in this Guide.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/quickstart_pipeline.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E:  Snowflake Feature Store uses Snowflake Enterprise Edition features and therefore requires Enterprise Edition or higher.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIn case you are new to some of the technologies mentioned above, here’s a quick summary with links to documentation.\u003C/p\u003E\n","\u003Ch3\u003EWhat is Snowpark?\u003C/h3\u003E\n","\u003Cp\u003ESnowpark is the set of libraries and code execution environments that run Python and other programming languages next to your data in Snowflake.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EClient Side Libraries\u003C/strong\u003E - Snowpark libraries can be downloaded, installed and used from any client-side notebook or IDE and are used for code development and deployment. Libraries include the [Snowpark ML API)[https://docs.snowflake.com/en/developer-guide/snowpark-ml/overview#installing-snowpark-ml], which provides Python APIs for machine learning workflows in Snowflake.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECode Execution Environments\u003C/strong\u003E - Snowpark provides elastic compute environments for secure execution of your code in Snowflake. Runtime options include Python, Java, and Scala in warehouses, container runtimes for out-of-the-box distributed processing with CPUs or GPUs using any Python framework, or custom runtimes brought in from Snowpark Container Services to execute any language of choice with CPU or GPU compute.\u003C/p\u003E\n","\u003Cp\u003ELearn more about Snowpark \u003Ca href=\"/snowpark/\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EWhat is Snowflake ML?\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-ml/overview\"\u003ESnowflake ML\u003C/a\u003E is the integrated set of capabilities for end-to-end machine learning in a single platform on top of your governed data. Snowflake ML can be used for fully custom and out-of-the-box workflows. For ready-to-use ML, analysts can use \u003Ca href=\"https://docs.snowflake.com/en/guides-overview-ml-functions\"\u003EML Functions\u003C/a\u003E to shorten development time or democratize ML across your organization with SQL from Studio, our no-code user interface. For custom ML, data scientists and ML engineers can easily and securely develop and productionize scalable features and models without any data movement, silos or governance tradeoffs.\u003C/p\u003E\n","\u003Cp\u003ECapabilities for custom ML include:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake Notebooks for a familiar, easy-to-use notebook interface that blends Python, SQL, and Markdown\u003C/li\u003E\u003Cli\u003EContainer Runtimes for distributed CPU and GPU processing out of the box from Snowflake Notebooks\u003C/li\u003E\u003Cli\u003ESnowpark ML Modeling for feature engineering and model training with familiar Python frameworks\u003C/li\u003E\u003Cli\u003ESnowflake Feature Store for continuous, automated refreshes on batch or streaming data\u003C/li\u003E\u003Cli\u003ESnowflake Model Registry to manage models and their metadata\u003C/li\u003E\u003Cli\u003EML Lineage to trace end-to-end feature and model lineage (currently in private preview)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ETo get started with Snowflake ML, developers can use the Python APIs from the \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-ml/index\"\u003ESnowpark ML library\u003C/a\u003E to interact with all development and operations features across the ML workflow.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/snowflake_ml.png\" alt=\"snowpark_ml\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis guide will focus on\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/LIMITEDACCESS/snowflake-feature-store#generating-datasets-for-training\"\u003ESnowflake Feature Store\u003C/a\u003E, which enables the creation of feature-engineering pipelines, and efficient, time-accurate retrieval of features for model training and inference. Snowflake Feature Store enables data engineers, data scientists and ML engineers to centralize the curation, maintenance and sharing of features that can be used in machine-learning models.\u003C/p\u003E\n","\u003Cp\u003EYou can create multiple Feature-Stores within a Snowflake account, for example organized by environment or business unit.  Features are automatically maintained using Snowflake Dynamic Tables to ensure that the feature-engineering pipeline keeps features updated to the required level of freshness. When retrieving features for training or inference, feature-store ensures that the feature values are retrieved at the required point-in-time using Snowflake AsOf joins.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/snowflake_feature_store.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-ml/snowpark-ml-modeling\"\u003ESnowpark ML Modeling\u003C/a\u003E, which enables the use of popular Python ML frameworks, such as scikit-learn and XGBoost, for feature pre-processing and model training without the need to move data out of Snowflake.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/snowpark_ml_modeling.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-ml/snowpark-ml-mlops-model-registry\"\u003ESnowflake Model Registry\u003C/a\u003E, which provides scalable and secure model management of ML models in Snowflake, regardless of origin.\nUsing these features, you can build and operationalize a complete ML workflow, taking advantage of Snowflake's scale and security features.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/snowpark_ml_arch.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to perform data and feature engineering tasks using Snowpark DataFrames and APIs\u003C/li\u003E\u003Cli\u003EHow to create a Snowflake Feature Store, and the key objects within it like Entities and Feature Views\u003C/li\u003E\u003Cli\u003EHow to train ML model using Snowpark ML in Snowflake using features and training data derived from Snowflake Feature Store\u003C/li\u003E\u003Cli\u003EHow to register ML model and use it for inference from Snowflake Model Registry.\u003C/li\u003E\u003Cli\u003EHow to operationalize an end-to-end feature-engineering and ML model to perform micro-batch inference.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://git-scm.com/book/en/v2/Getting-Started-Installing-Git\"\u003EGit\u003C/a\u003E installed\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.python.org/downloads/\"\u003EPython 3.11\u003C/a\u003E installed\n\u003Cul\u003E\u003Cli\u003ENote that you will be creating a Python environment with 3.11 in the \u003Cstrong\u003EGet Started\u003C/strong\u003E step\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EA Snowflake account with \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#using-third-party-packages-from-anaconda\"\u003EAnaconda Packages enabled by ORGADMIN\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EA Snowflake account login with the ACCOUNTADMIN role. If you have this role in your environment, you may choose to use it. If not, you will need to use a different role that has the ability to create database, schema, tables, dynamic tables, tags,  stages, tasks, user-defined functions, and stored procedures OR 3) Use an existing database and schema in which you have been granted the permissions to create the afore mentioned objects.\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIMPORTANT\u003C/strong\u003E: Before proceeding, make sure you have a Snowflake account with Anaconda packages enabled by ORGADMIN as described \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages#getting-started\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAn end-to-end Machine Learning use-case including data-retrieval, feature-engineering and management, model-training and storage and operationalization as a scheduled inference process.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup Python\u003C/h2\u003E\n","\u003Ch3\u003EGet Started\u003C/h3\u003E\n","\u003Cp\u003EThis section covers cloning of the GitHub repository and setting up your Snowpark for Python environment.\u003C/p\u003E\n","\u003Ch3\u003EClone GitHub Repository\u003C/h3\u003E\n","\u003Cp\u003EThe very first step is to clone the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store\"\u003EGitHub repository\u003C/a\u003E. This repository contains all the code you will need to successfully complete this Guide.\u003C/p\u003E\n","\u003Cp\u003EUsing HTTPS:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003Egit clone https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store.git\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOR, using SSH:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003Egit clone git@github.com:Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store.git\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESnowpark for Python\u003C/h3\u003E\n","\u003Cp\u003ETo complete the Guide we will need a Python environment installed with the prerequisite packages.\u003C/p\u003E\n","\u003Ch4\u003ELocal Python Environment Installation\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 1:\u003C/strong\u003E Download and install the miniconda installer from \u003Ca href=\"https://conda.io/miniconda.html\"\u003Ehttps://conda.io/miniconda.html\u003C/a\u003E. \u003Cem\u003E(OR, you may use any other Python environment with Python 3.10, for example, \u003Ca href=\"https://virtualenv.pypa.io/en/latest/\"\u003Evirtualenv\u003C/a\u003E)\u003C/em\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 2:\u003C/strong\u003E Open a new terminal window and execute the following commands in the same terminal window.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 3:\u003C/strong\u003E Create Python 3.10 conda environment called \u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E with initial required packages by running the following command in the same terminal window\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econda create -n py-snowpark_df_ml_fs python=3.10 numpy pandas pyarrow jupyterlab tabulate --override-channels -c https://repo.anaconda.com/pkgs/snowflake\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote: if you are installing onto a Apple Mac with Apple silicon you will need to use the following instead\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003ECONDA_SUBDIR=osx-64 conda create -n py-snowpark_df_ml_fs python=3.10 numpy pandas pyarrow jupyterlab tabulate --override-channels -c https://repo.anaconda.com/pkgs/snowflake\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 4:\u003C/strong\u003E Activate conda environment \u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E by running the following command in the same terminal window\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econda activate py-snowpark_df_ml_fs\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote: if you are installing onto a Apple Mac with Apple silicon you will need to use the following command after you have activated the Conda environment.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econda config --env --set subdir osx-64\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 5:\u003C/strong\u003E Install Snowpark Python, Snowpark ML, and other libraries in conda environment \u003Cstrong\u003Epy-snowpark_df_ml_fs\u003C/strong\u003E from \u003Ca href=\"https://repo.anaconda.com/pkgs/snowflake/\"\u003ESnowflake Anaconda channel\u003C/a\u003E by running the following command in the same terminal window\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econda install -c https://repo.anaconda.com/pkgs/snowflake snowflake-snowpark-python=1.16.0 snowflake-ml-python=1.5.1 notebook\npip install snowflake\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 6:\u003C/strong\u003E Create a Jupyter Kernel to represent the environment that we have just created using\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eipython kernel install --user --name=py-snowpark_df_ml_fs\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 7:\u003C/strong\u003E Make sure you are in the top level directory for this Guide, and start Jupyter to test the it is setup correctly\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ejupyter lab\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFollow the instructions output by Jupyter in the console to open jupyter lab in your browser, if it has not automatically open a tab in your browser.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 8:\u003C/strong\u003E\nYou will need a Snowflake login and to setup a connection for use within the provided Jupyter Notebooks.\u003C/p\u003E\n","\u003Cp\u003EThere are several options for creating a Snowpark connection.  You can use the method described here in \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-ml/overview#connecting-to-snowflake\"\u003Esnowpark connection\u003C/a\u003E.  You will need to create an entry in your \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowsql-config\"\u003ESnowSQL configuration file\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EAlternatively, you can update \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store/connection.json\"\u003Econnection.json\u003C/a\u003E with your Snowflake account details and credentials.\u003C/p\u003E\n","\u003Cp\u003EHere's a sample \u003Cem\u003E\u003Cstrong\u003Econnection.json\u003C/strong\u003E\u003C/em\u003E based on the object names mentioned in \u003Cstrong\u003ESetup Database Environment\u003C/strong\u003E step.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "account"   : "<your_account_identifier_goes_here>",\n  "user"      : "<your_username_goes_here>",\n  "password"  : "<your_password_goes_here>",\n  "role"      : "FS_QS_ROLE",\n  "warehouse" : "TPCXAI_SF0001_WH",\n  "database"  : "TPCXAI_SF0001_QUICKSTART_INC",\n  "schema"    : "TRAINING"\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can then read the parameters into Python with\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econnection_parameters = json.load(open('connection.json'))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAnd connect to the database using:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esession = Session.builder.configs(connection_parameters).create()\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: For the \u003Cstrong\u003Eaccount\u003C/strong\u003E parameter above, specify your \u003Cstrong\u003Eaccount identifier\u003C/strong\u003E and do not include the snowflakecomputing.com domain name. Snowflake automatically appends this when creating the connection. For more details on that, \u003Ca href=\"https://docs.snowflake.com/en/user-guide/admin-account-identifier.html\"\u003Erefer to the documentation\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E_\u003Cem\u003ENote\u003C/em\u003E: Initially, before you have executed the Setup Notebook below, you will need to use pre-existing objects (database, schema, warehouse) you have access to in your account. Once we have setup the Database environment with the Notebook below you can update \u003Ccode\u003Econnection.json\u003C/code\u003E with the objects created.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup Database Environment\u003C/h2\u003E\n","\u003Cp\u003EWe will now need to setup the Database to mimic development and production databases environments, where new data is being regularly ingested to maintain the latest data from source systems.  Snowflakes Feature Store automates the maintenance of the feature-engineering pipelines from source tables in Snowflake which we want to observe in this Guide.\u003C/p\u003E\n","\u003Cp\u003EWe are using the dataset from \u003Ca href=\"https://www.tpc.org/tpcx-ai/default5.asp\"\u003ETPCX-AI\u003C/a\u003E and it's usecases for this Guide example.  We will use Usecase 1, which performs a customer segmentation using customer Orders, Line-Items and Order-Returns tables.  We will also load the Customer data should you wish to further enrich the use-case with additional customer data.\u003C/p\u003E\n","\u003Cp\u003EFor each table TPCX-AI provides three parts to the data:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ETRAINING\u003C/strong\u003E (\u003Cem\u003EDevelopment\u003C/em\u003E)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESCORING\u003C/strong\u003E (\u003Cem\u003ETest\u003C/em\u003E)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESERVING\u003C/strong\u003E (\u003Cem\u003EProduction\u003C/em\u003E)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWe can think of Training as representing our Development environment where we maintain a sample of historical data that is used for development purposes.  The Scoring environment represents a Test environment where we store data not used in Development to validate our code is working. In Machine Learning this data is used to validate that the features and models we have developed, will generalise well to typical data not observed during the development (training) process.  Finally Serving represents the Production environment where we have new 'live' data arriving from source systems. It is on this new data that we want to operationalize our machine-learning pipeline to perform predictions/inference on new data, taking actions as a result.\u003C/p\u003E\n","\u003Cp\u003EWe will create a Snowflake Database (\u003Cstrong\u003ETPCXAI_SF0001_QUICKSTART\u003C/strong\u003E) to hold three schemas (\u003Cstrong\u003ETRAINING\u003C/strong\u003E, \u003Cstrong\u003ESCORING\u003C/strong\u003E, \u003Cstrong\u003ESERVING\u003C/strong\u003E) representing the environments and different data subsets for each table.  This database will contain static source data loaded from Parquet files.  From this we will create another Database (\u003Cstrong\u003ETPCXAI_SF0001_QUICKSTART_INC\u003C/strong\u003E) with the same schemas, and objects, that will incrementally ingest data from \u003Cstrong\u003ETPCXAI_SF0001_QUICKSTART\u003C/strong\u003E to mimic a 'Live' environment.  We also have the \u003Cstrong\u003ECONFIG\u003C/strong\u003E schema that holds the Stages, File-Formats, Tasks, Streams etc, that populate and maintain the data in from source and between the two database.\u003C/p\u003E\n","\u003Cp\u003ETo simplify the overall creation and setup of these databases and the required objects within them we provide a Jupyter Notebook (\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store/tree/main/Step01_TPCXAI_UC01_Setup.ipynb\"\u003EStep01_TPCXAI_UC01_Setup.ipynb\u003C/a\u003E)\u003C/p\u003E\n","\u003Cp\u003EThe Notebook uses the SnowSQL configuration method of creating the database connection.  If you prefer you can use the \u003Ccode\u003Econnections.json\u003C/code\u003E method by adjusting this file :\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Econnections.json\u003C/code\u003E : Containing the connection credentials for the Account that you are using. Customise as appropriate to your account.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYou will need to adjust the Notebook to load the file into python:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econnection_parameters = json.loads('connections.json)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWithin your Jupyter session you should see the Notebook file (\u003Ccode\u003EStep01_TPCXAI_UC01_Setup.ipynb\u003C/code\u003E) in the file-browswer.  Open the Notebook, select (top-right) the Conda Environment/Jupyter Kernel (\u003Ccode\u003Epy-snowpark_df_ml_fs\u003C/code\u003E) that we created earlier for the Notebook.\u003C/p\u003E\n","\u003Cp\u003EYou will need to do the same for the other Notebooks used in this Guide\u003C/p\u003E\n","\u003Cp\u003EStep through the Notebook running the cells to setup the Database environment.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Pay particular attention to the third code cell, and make any adjustments needed for your account/environment and user. See below:\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/setup_adjustment_cell.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E If you are lacking specific elevated privileges needed for some of the operations, you may encounter issues executing some of the steps.  You will need to work with an Account administrator to resolve these.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EOnce you have successfully executed the Notebook, you can check in Snowsight that your environment has been created.  The Database viewer should look something like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/Database_Hierarchy_post_setup.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou will find a similar hierarchy under the TPCXAI_SF0001_QUICKSTART_INC\u003C/p\u003E\n","\u003Cp\u003EWe can see that a number of Streams & Tasks have been created that are running frequently to incrementally add new data into your \u003Cstrong\u003ETPCXAI_SF0001_QUICKSTART_INC\u003C/strong\u003E database tables.  We can check that these are running by looking at Snowsight Task History under Monitoring on the left-hand side.  Once it has been running for a while it should something like this.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E If you have only just created the Tasks you will see less execution history.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/task_history.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can drill into the Task details from the Data viewer.  For example:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/append_scoring_lineitem_task.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can see that the Tasks are set to execute every minute if new data is available.  Feel free to reduce the frequency should you want to, although you will then need to wait longer to observe changes in FeatureViews when you create them in the Guide.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E If you are going to leave these running for any length of time, assuming you are completing the Guide over a few days. You should also \u003Ccode\u003ESUSPEND\u003C/code\u003E the Tasks to minimise costs.  You can \u003Ccode\u003ERESUME\u003C/code\u003E them when you want to restart, and they will pick up where they left off, loading any additional data for the interim period.  You can do this via the Elipsis in the top-right corner, or programmatically with SQL if you prefer.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFeature Engineering & Model Training\u003C/h2\u003E\n","\u003Ch3\u003EKey Snowfake Feature Store API Concepts\u003C/h3\u003E\n","\u003Cp\u003EIn the Snowflake Feature Store, as typical of other Feature Store solutions:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EEntities\u003C/strong\u003E - define the business-entity and the level that we want to gather data and develop ML models at. (e.g. store or/and product key etc).\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFeatures\u003C/strong\u003E are defined and grouped within \u003Cstrong\u003EFeatureViews\u003C/strong\u003E.  In Snowflake Feature Store features are columns, or column-expressions defined via the Snowpark for Python dataframe api, or via SQL directly.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFeatureViews\u003C/strong\u003E are associated (defined) for one or more \u003Cstrong\u003EEntities\u003C/strong\u003E. A \u003Cstrong\u003EFeatureView\u003C/strong\u003E can be defined with 1:n Entities, but typically only one.\nSeveral (many) \u003Cstrong\u003EFeatureViews\u003C/strong\u003E may contain Features for the same Entity. FeatureViews tend to get defined based on the data-source they are derived from, the data’s refresh or calculation frequency.  A \u003Cstrong\u003EFeatureView\u003C/strong\u003E us defined via a Snowpark Dataframe (or SQL expression) enabling a complex pipeline to be used.\u003C/li\u003E\u003Cli\u003EThe \u003Cstrong\u003EEntity\u003C/strong\u003E (key columns) are used to join \u003Cstrong\u003EFeatureViews\u003C/strong\u003E together when needed to gather features from multiple \u003Cstrong\u003EFeatureViews\u003C/strong\u003E within a single training or inference dataset, or derive new \u003Cstrong\u003EFeatureViews\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003EFeatureSlice\u003C/strong\u003E provides a way of creating a subset of the Features from a single \u003Cstrong\u003EFeatureViews\u003C/strong\u003E when needed.  It can be used within the API, pretty much anywhere the \u003Cstrong\u003EFeatureViews\u003C/strong\u003E can be used.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFeatureViews\u003C/strong\u003E and \u003Cstrong\u003EFeatureSlices\u003C/strong\u003E can be merged (via merge_features) to gather features together and create a new \u003Cstrong\u003EFeatureView\u003C/strong\u003E. For example, all the features for a given \u003Cstrong\u003EEntity\u003C/strong\u003E could be gathered via the merge into a single.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYou can  learn more about Snowflake Feature Store in this \u003Ca href=\"https://docs.snowflake.com/developer-guide/snowpark-ml/feature-store/overview\"\u003Esection\u003C/a\u003E of the documentation.\u003C/p\u003E\n","\u003Ch3\u003ESetting up the Feature Store\u003C/h3\u003E\n","\u003Cp\u003EWith our database established, we are now ready to get started on Feature Engineering and Model Training.  Open the Jupyter Notebook (\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store/tree/main/Step02_TPCXAI_UC01_Feng_and_Train.ipynb\"\u003EStep02_TPCXAI_UC01_Feng_and_Train.ipynb\u003C/a\u003E) to get started.  Adjust the Notebook connection method if needed for your environment.\u003C/p\u003E\n","\u003Cp\u003EYou can step through the Notebook to create Feature Engineering Pipeline, Feature-Store & Model-Registry, interact with the Feature Store and train a model using Snowpark ML.  We will describe some of the key steps in the Notebook below.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E You can now adjust your \u003Cem\u003E\u003Cstrong\u003Econnection.json\u003C/strong\u003E\u003C/em\u003E file, to reflect the database, schema and warehouse that you have created in the prior Step.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E As before , pay particular attention to the third code cell, and make any adjustments needed for your account/environment and user. See below:\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/setup_adjustment_cell.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe use a couple of helper functions \u003Ccode\u003Ecreate_FeatureStore\u003C/code\u003E and \u003Ccode\u003Ecreate_ModelRegistry\u003C/code\u003E imported from \u003Ccode\u003Euseful_fns.py\u003C/code\u003E to create our Feature-Store and Model-Registry.  These functions check for the prior creation of these, and create them if they are not already created.  If they are already created they create a python class-instance referencing them.\u003C/p\u003E\n","\u003Cp\u003ECreating the Feature Store creates a schema (with the provided name \u003Ccode\u003E_TRAINING_FEATURE_STORE\u003C/code\u003E ) in our (\u003Cstrong\u003ETPCXAI_SF0001_QUICKSTART_INC\u003C/strong\u003E) database. This schema contains all the objects created through your interactions with the Python Feature Store API.  Database objects are tagged with Feature Store related tags to denote that they are part of the Feature Store.  These tags are used by Snowsight to discover and present Feature Store objects.  The two main other types of database objects that you will see being created are Dynamic Tables and Views.  We will describe these in more detail later in this section.\u003C/p\u003E\n","\u003Cp\u003EThe diagram below depicts the Feature Store information-architecture and how objects in the Python API relate to Database objects.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/fs_information_architecture.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EEntity creation\u003C/h3\u003E\n","\u003Cp\u003ENow we have our Feature Store created we can create the Entity that we will be using for this use case.  We are building a customer segmentation process, so we will primarily be deriving features at the Customer level.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ecustomer_entity = Entity(name="CUSTOMER", join_keys=["O_CUSTOMER_SK"],desc="Primary Key for CUSTOMER")\nfs.register_entity(customer_entity)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe code above defines an instance of the Feature Store entity.  The \u003Ccode\u003Eregister_entity\u003C/code\u003E method creates the object in the database. Entities are created as database tags.  Other Feature Store objects that are created that relate to this Entity are tagged with this tag as we will see shortly.\u003C/p\u003E\n","\u003Cp\u003EWe can \u003Ccode\u003Elist_entities()\u003C/code\u003E which returns a Snowpark dataframe that can be \u003Ccode\u003E.show()\u003C/code\u003E or filtered as needed.  We can also provide SQL wild-card expressions within \u003Ccode\u003Elist_entities()\u003C/code\u003E for filtering by name elements.\u003C/p\u003E\n","\u003Ch3\u003EFeature Engineering Pipeline\u003C/h3\u003E\n","\u003Cp\u003EFeature engineering pipelines are defined using Snowpark dataframes (or SQL expressions).  In the \u003Ccode\u003Efeature_engineering_fns.py\u003C/code\u003E file We have created two feature engineering functions to create our pipeline :\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Euc01_load_data\u003C/strong\u003E(order_data: DataFrame, lineitem_data: DataFrame, order_returns_data: DataFrame) -> DataFrame\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Euc01_pre_process\u003C/strong\u003E(data: DataFrame) -> DataFrame\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Ccode\u003Euc01_load_data\u003C/code\u003E, takes the source tables, as dataframe objects, and joins them together, performing some data-cleansing by replacing NA's with default values. It returns a dataframe as it's output.\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003Euc01_pre_process\u003C/code\u003E, takes the dataframe output from \u003Ccode\u003Euc01_load_data\u003C/code\u003E  and performs aggregation on it to derive some features that will be used in our segmentation model.  It returns a dataframe as output, which we will use to provide the feature-pipeline definition within our FeatureView.\u003C/p\u003E\n","\u003Cp\u003EIn this way we can build up a complex pipeline step-by-step and use it to derive a FeatureView, that will be maintained as a pipeline in Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EFeatureView Creation\u003C/h3\u003E\n","\u003Cp\u003EWe will use the dataframe that we defined in the prior step for the FeatureView we are creating. The FeatureView will create a Dynamic Table in our Feature Store schema.  We could use the dataframe directly within the definition of the FeatureView.  The SQL query generated from Snowpark through the dataframe definition, is machine generated and not necessarily easy for a human to parse, when used and viewed within the Dynamic Table.  Therefore optionally we can parse the SQL and format it to something more human readable.  We use the \u003Ccode\u003Esqlglot\u003C/code\u003E Python package to do this.  We created a simple function that takes the raw SQL generated from Snowpark, parses it and returns a formatted SQL statement. Depending on your preference, you can choose to convert sub-selects to common-table-expressions.\u003C/p\u003E\n","\u003Cp\u003EThe image below shows the FeatureView creation process, and calls out a few key elements of the FeatureView definition.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/fv_creation.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESimilarly to the Entity creation,  this is a two step process, first creating the python instance, and then registering the instance to create an object in the database.  We provide the feature view name, version, description, and individual descriptions for each feature. We can create new versions of a Feature as it evolves, for example if the definition of some of the Features within change. Once created a version is immutable, unless a forced replacement is needed and invoked via \u003Ccode\u003Eoverwrite = True\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EWe add the Entity (\u003Ccode\u003ECUSTOMER\u003C/code\u003E) that we created earlier. This allows the relationship, and join keys, available in the Feature View to be defined.  We will see how this is used when we want to retrieve Features from the feature store.\u003C/p\u003E\n","\u003Cp\u003EIf we provide \u003Ccode\u003Erefresh_freq\u003C/code\u003E [optional argument] the database object that is created from the Feature View definition is a Dynamic Table, otherwise a View is created.  In the case of a Dynamic Table, the table is initially populated with data, and from that point forward incrementally maintained when new data lands in the source tables. As we have created incrementing data sources, we can observe this incremental processing being applied to the table, using Snowsight's Dynamic Table observation features.  See the image below.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/fv_dynamic_table.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe Snowsight UI also contains a new section supporting Feature Store discovery and observability. can be used to search, discover and review available Features for a given machine-learning task, enabling re-use of features across multiple models, and expediting the time required to implement machine-learning projects. The below image shows the Snowsight UI Feature Store section, Entity level view.  We can see the FeatureView that we have created, under the Customer Entity.  We can also see other Entities, and FeatureViews that have been created for other use-cases within this Feature Store.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/FeatureStore_Snowsight.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe feature-store provides lineage of data from source tables, through feature-engineering to model and model-inference enabling users to understand the broader impact in data-quality issues in source data, answering questions like:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Ewhat features and models are derived from this source/table.\u003C/li\u003E\u003Cli\u003Ewhat data-engineering and transformations are applied to derive this feature.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EFeature Retrieval\u003C/h3\u003E\n","\u003Cp\u003ENow we have a Feature View with data being maintained within it, we can use it to retrieve data for model-training, and model-inference.  The Feature Store enables feature-values to be retrieved for a given set of Entity-keys, relative to a reference point-in-time.  Under the covers the Feature Store uses the new SQL AsOf join functionality in Snowflake to efficiently retrieve the requested features across the FeatureViews.  The Entity-Keys and Timestamps are provided as a dataframe, which we call a Spine.  The Spine can be defined using Snowpark Dataframe funcionality, or via a SQL expression.\u003C/p\u003E\n","\u003Cp\u003EFor example, we can create the spine with the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Espine_sdf =  fv_uc01_preprocess.feature_df.group_by('O_CUSTOMER_SK').agg( F.max('LATEST_ORDER_DATE').as_('ASOF_DATE')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe can then use the Spine to create a Dataset.  Datasets are a new type of data-object in Snowflake that allows immutable datasets that are optimised for Machine Learning to be persisted and read directly into common machine learning frameworks like scikit-learn, Tensorflow and Pytorch.  We create the Dataset with the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etraining_dataset = fs.generate_dataset( name = 'UC01_TRAINING',\n                                        spine_df = spine_sdf, features = [fv_uc01_preprocess], \n                                        spine_timestamp_col = 'ASOF_DATE'\n                                        )  \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe Dataset can also be converted into other object types if needed.  For example, we can create a Snowpark Dataframe or a Pandas dataframe from the Dataset with the following code.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Snowpark Dataframe\ntraining_dataset_sdf = training_dataset.read.to_snowpark_dataframe()\n# Pandas Dataframe\ntraining_dataset_pdf = training_dataset.read.to_pandas()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EFit a Snowpark-ML Kmeans Model\u003C/h3\u003E\n","\u003Cp\u003EWe use the training Dataset we created in the previous step to fit a Snowpark-ML Kmeans model. You can read more about Snowpark ML Model Development in this \u003Ca href=\"https://docs.snowflake.com/developer-guide/snowpark-ml/modeling\"\u003Esection\u003C/a\u003E of the documentation. To do so we define our model fitting pipeline as a function that includes some feature pre-processing to scale our input variables using min-max scaling.  These transformations need to be applied at model time, as they capture the global state (e.g. minimum and maximum values for columns) of our training sample.\u003C/p\u003E\n","\u003Cp\u003EWe fit the model and log it to the Model Registry that we created earlier. You can read more about Snowflake ML Model Registry in this \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-ml/model-registry/overview\"\u003Esection\u003C/a\u003E of the documentation. As with the Snowflake Feature Store, models in the registry are versioned. When we fit our model with Snowpark ML, using the Feature Store and register the model in the Registry, Snowflake captures the full lineage from source tables through to the model. We can interogate the lineage information to understand what models might be impacted by a data-quality problem in our source tables for example.\u003C/p\u003E\n","\u003Cp\u003EModel fitting and optimisation is typically a highly iterative process where different subsets of features, over varying data samples are used in combination with different sets of model hyper-parameters.  With feature store and model lineage and Model Registry all the the information related to each fitting run is captured, so that we have full Model Reproducibility and Discovery should we need. During this process we would normally check our model against a test dataset, to generate test-scores for the model.   Many more sophisticated  validation techniques exist, but are beyond the scope of this Guide.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EWithin-Cluster Sum of Squares\u003C/li\u003E\u003Cli\u003ESilhouette Score\u003C/li\u003E\u003Cli\u003EGap Statistics\u003C/li\u003E\u003Cli\u003ECross-Validation\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIn the Notebook we have simply plotted the clusters to review visually.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/cluster_plot.png\" alt=\"snowpark_ml\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis ends the model-development phase.  From this point on, we assume that the simple model we created is good enough for production and operationalization.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EModel Operationalization in Production\u003C/h2\u003E\n","\u003Cp\u003EWe will use a new Notebook (\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store/tree/main/Step03_TPCXAI_UC01_Operations.ipynb\"\u003EStep03_TPCXAI_UC01_Operations.ipynb\u003C/a\u003E) for the Model Operationalisation stage.  This may be created by a different person/role in the organisation. For example a data or ML engineer.  Open the Notebook and adjust the Notebook connection method if needed for your environment.\u003C/p\u003E\n","\u003Cp\u003EThis notebook shows how you can easily replicate the training feature-engineering pipeline, created during model development, in the SERVING (\u003Cem\u003EProduction\u003C/em\u003E) schema. We then create an inference function and deploy a new FeatureView that schedules ongoing inference from new data flowing through our Feature Engineering pipeline from our source data.  We can monitor the production pipeline (Dynamic Tables) using the same tools that we have already seen in the Feature Engineering and Model Training phase.\u003C/p\u003E\n","\u003Ch3\u003ERecreate the Feature-Engineering pipeline\u003C/h3\u003E\n","\u003Cp\u003EWe created FeatureViews in our \u003Ccode\u003E_TRAINING_FEATURE_STORE\u003C/code\u003E (Development) schema.  We will create another Feature Store (\u003Ccode\u003E_SERVING_FEATURE_STORE\u003C/code\u003E) for the Production environment. This will hold new FeatureViews created with the same definition, but running over Production data.  We can easily modify the tables that are referenced in production, versus development, by changing the Schema in the dataframe definition. We assume that the database tables are defined identically between development and production.\u003C/p\u003E\n","\u003Cp\u003EFor this Guide we have chosen to share the Model-Registry across all environments as we will use the model we trained in Development, in Production for inference.  Alternatively, we could also create a new seperate Model Registry for production and Copy models between environments, or retrain the Model in production with appropriate checks and balances to ensure the new model over production data is still good for operationalisation.\u003C/p\u003E\n","\u003Cp\u003EWhen we register our model in the Model Registry it packages it as a Python function which enables direct access from \u003Ca href=\"https://docs.snowflake.com/developer-guide/snowpark-ml/model-registry/overview#calling-model-methods\"\u003EPython\u003C/a\u003E or from \u003Ca href=\"https://docs.snowflake.com/sql-reference/commands-model#label-snowpark-model-registry-model-methods\"\u003ESQL\u003C/a\u003E.  This allows the creation of an inference Feature View that uses the model directly for prediction from our Feature Engineering pipeline,\u003C/p\u003E\n","\u003Ch3\u003ECreate an Inference Feature view\u003C/h3\u003E\n","\u003Cp\u003EWe define our model inference function, which we pass our feature values and model into.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef uc01_serve(featurevector, km4_purchases) -> DataFrame:\n    return km4_purchases.run(featurevector, function_name="predict")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe define a dataframe that reads all the records from our feature engineering pipeline. When used within the FeatureView, the Dynamic Table that gets created, will incrementally process change data once the initial Dynamic Table has been created.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Einference_input_sdf = fs.read_feature_view(fv_uc01_preprocess)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe then create a FeatureView that will compute Inference on incremental data in the feature engineering pipeline, keeping an up to date set of customer segments through time.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E## Create & Register Inference-FeatureView to run scheduled Inference\ninf_fvname = "FV_UC01_INFERENCE_RESULT"\ninf_fv_version = "V_1"\n\ninference_features_desc = { "FREQUENCY":"Average yearly order frequency",\n                              "RETURN_RATIO":"Average of, Per Order Returns Ratio.  Per order returns ratio : total returns value / total order value", \n                              "RETURN_RATIO_MMS":f"Min/Max Scaled version of RETURN_RATIO using Model Registry ({tpcxai_database}_MODEL_REGISTRY) Model ({mv.model_name}) Model-Version({mv.version_name}) Model Comment ({mv.comment})",\n                              "FREQUENCY_MMS":f"Min/Max Scaled version of FREQUENCY using Model Registry ({tpcxai_database}_MODEL_REGISTRY) Model ({mv.model_name}) Model-Version({mv.version_name})  Model Comment ({mv.comment}",\n                              "CLUSTER":f"Kmeans Cluster for Customer Clustering Model (UC01) using Model Registry ({tpcxai_database}_MODEL_REGISTRY) Model ({mv.model_name}) Model-Version({mv.version_name})  Model Comment ({mv.comment}"}\n\ntry:\n   fv_uc01_inference_result = fs.get_feature_view(name= inf_fvname, version= inf_fv_version)\nexcept:\n   fv_uc01_inference_result = FeatureView(\n         name= inf_fvname, \n         entities=[customer_entity], \n         feature_df=inference_result_sdf,\n         refresh_freq="60 minute",  # <- specifying optional refresh_freq creates FeatureView as Dynamic Table, else created as View.         \n         desc="Inference Result from kmeans model for Use Case 01").attach_feature_desc(inference_features_desc)\n   \n   fv_uc01_inference_result = fs.register_feature_view(\n         feature_view=fv_uc01_inference_result, \n         version= inf_fv_version, \n         block=True\n   )\n   print(f"Inference Feature View : fv_uc01_inference_result_{inf_fv_version} created")   \nelse:\n   print(f"Inference Feature View : fv_uc01_inference_result_{inf_fv_version} already created")\nfinally:\n   fs_serving_fviews = fs.list_feature_views().filter(F.col("NAME") == inf_fvname ).sort(F.col("VERSION").desc())\n   fs_serving_fviews.show() \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn the FeatureView definition we have embellished our feature comments with the model name and model-version to make it directly available in the database object definition, but this information can also be derived through the feature and model registry lineage api.\u003C/p\u003E\n","\u003Cp\u003EOnce we have created the FeatureView we can retrieve inferences from it.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efv_uc01_inference_result.feature_df.sort(F.col("LATEST_ORDER_DATE").desc()).show(100)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe can monitor how CUSTOMERS behaviour (segment) changes over time and take targetted action as a result.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClean Up\u003C/h2\u003E\n","\u003Cp\u003EOnce you have completed this Guide and no longer need the databases and objects created by it you will want to clean up.  We provide a Notebook that does this. \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store/tree/main/Step04_TPCXAI_UC01_Cleanup.ipynb\"\u003EStep04_TPCXAI_UC01_Cleanup.ipynb\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003EIf you want to keep the data, but shut down the Tasks and Dynamic Tables to minimise compute cost, you will need to go to each Task and Dynamic Table to \u003Ccode\u003ESUSPEND\u003C/code\u003E them.  This can be done in the Snowsight UI, or you can use the applicable SQL commands to achieve the same.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully performed Feature Engineering using Snowpark, made use of Snowflake Feature Store to publish and maintain features in a development and production environment. You've learnt how you can deploy a model from the Snowflake Model Registry and combine it with a feature-engineering pipeline in Feature Store to operationalise an incremental inference process in Snowflake ML.\u003C/p\u003E\n","\u003Cp\u003EWe would love your feedback on this Guide! Please submit your feedback using this \u003Ca href=\"https://forms.gle/JeZWYwkCMk3gty7D7\"\u003EFeedback Form\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to analyze data and perform data engineering tasks using Snowpark DataFrames and APIs\u003C/li\u003E\u003Cli\u003EHow to use open-source Python libraries from curated Snowflake Anaconda channel\u003C/li\u003E\u003Cli\u003EHow to create Snowflake Tasks to automate data pipelines\u003C/li\u003E\u003Cli\u003EHow to train ML model using Snowpark ML in Snowflake\u003C/li\u003E\u003Cli\u003EHow to register ML model and use it for inference from Snowflake Model Registry\u003C/li\u003E\u003Cli\u003EHow to create Streamlit application that uses the ML Model for inference based on user input\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store\"\u003ESource Code on GitHub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/intro-to-machine-learning-with-snowpark-ml-for-python/\"\u003EIntro to Machine Learning with Snowpark ML\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/data-engineering-pipelines-with-snowpark-python/\"\u003EAdvanced: Snowpark for Python Data Engineering Guide\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/getting-started-snowpark-machine-learning/\"\u003EAdvanced: Snowpark for Python Machine Learning Guide\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/python/index.html\"\u003ESnowpark for Python Developer Guide\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/index.html\"\u003ESnowpark for Python API Reference\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/developer-guide/snowpark-ml/feature-store/overview\"\u003ESnowflake Feature Store\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/developer-guide/snowpark-ml/modeling\"\u003ESnowpark ML Modelling\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-ml/model-registry/overview\"\u003ESnowflake Model Registry\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->"],"title":"Advanced Guide to Snowflake Feature Store","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- --------------------------------------- --\u003E\n## Overview \n\nThis advanced guide walks through an end-to-end customer segmentation machine-learning use-case using Snowflake Feature Store and Model Registry.  By completing this guide, you will be able to go from ingesting raw data through to implementing a production inference data-pipeline with Snowflake ML to maintain customer segments.\n\nThe primary focus in this guide is the Snowflake Feature Stores functionality and how it integrates within the broader ecosystem within Snowflake ML.\n\nHere is a summary of what you will be able to learn in each step by following this Guide:\n\n- **Setup Environment**: Use stages and tables to ingest and organize raw data from S3 into Snowflake tables.  Setup a scheduled process to simulate incremental data-ingest into Snowflake tables.\n- **Feature Engineering**: Leverage Snowparks Python DataFrames to perform data cleansing, transformations such as group by, aggregate, pivot, and join to create features for machine learning.\n- **Feature Store**: Use Snowflakes Feature Store to register and maintain feature-engineering pipelines and understand how to monitor them once operational. \n- **Machine Learning**: Perform feature transformation and run ML Training in Snowflake using Snowpark ML. Register the trained ML model for inference from the Snowflake Model Registry\n- **Operationalise a Model**: Implementing a production inference data-pipeline to maintain customer segments as underlying customer behaviors change in source data.\n\nThe diagram below provides an overview of what we will be building in this Guide.\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/quickstart_pipeline.png)\n\n\u003E \n\u003E __Note__:  Snowflake Feature Store uses Snowflake Enterprise Edition features and therefore requires Enterprise Edition or higher.\n\nIn case you are new to some of the technologies mentioned above, here’s a quick summary with links to documentation.\n\n### What is Snowpark?\n\nSnowpark is the set of libraries and code execution environments that run Python and other programming languages next to your data in Snowflake.\n\n\n**Client Side Libraries** - Snowpark libraries can be downloaded, installed and used from any client-side notebook or IDE and are used for code development and deployment. Libraries include the [Snowpark ML API)[https://docs.snowflake.com/en/developer-guide/snowpark-ml/overview#installing-snowpark-ml], which provides Python APIs for machine learning workflows in Snowflake.\n\n**Code Execution Environments** - Snowpark provides elastic compute environments for secure execution of your code in Snowflake. Runtime options include Python, Java, and Scala in warehouses, container runtimes for out-of-the-box distributed processing with CPUs or GPUs using any Python framework, or custom runtimes brought in from Snowpark Container Services to execute any language of choice with CPU or GPU compute.\n\nLearn more about Snowpark [here](/snowpark/).\n\n### What is Snowflake ML?\n\n[Snowflake ML](https://docs.snowflake.com/en/developer-guide/snowpark-ml/overview) is the integrated set of capabilities for end-to-end machine learning in a single platform on top of your governed data. Snowflake ML can be used for fully custom and out-of-the-box workflows. For ready-to-use ML, analysts can use [ML Functions](https://docs.snowflake.com/en/guides-overview-ml-functions) to shorten development time or democratize ML across your organization with SQL from Studio, our no-code user interface. For custom ML, data scientists and ML engineers can easily and securely develop and productionize scalable features and models without any data movement, silos or governance tradeoffs.\n\nCapabilities for custom ML include:\n- Snowflake Notebooks for a familiar, easy-to-use notebook interface that blends Python, SQL, and Markdown\n- Container Runtimes for distributed CPU and GPU processing out of the box from Snowflake Notebooks\n- Snowpark ML Modeling for feature engineering and model training with familiar Python frameworks\n- Snowflake Feature Store for continuous, automated refreshes on batch or streaming data\n- Snowflake Model Registry to manage models and their metadata\n- ML Lineage to trace end-to-end feature and model lineage (currently in private preview)\n\nTo get started with Snowflake ML, developers can use the Python APIs from the [Snowpark ML library](https://docs.snowflake.com/en/developer-guide/snowpark-ml/index) to interact with all development and operations features across the ML workflow.\n\n![snowpark_ml](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/snowflake_ml.png)\n\nThis guide will focus on\n\n- [Snowflake Feature Store](https://docs.snowflake.com/LIMITEDACCESS/snowflake-feature-store#generating-datasets-for-training), which enables the creation of feature-engineering pipelines, and efficient, time-accurate retrieval of features for model training and inference. Snowflake Feature Store enables data engineers, data scientists and ML engineers to centralize the curation, maintenance and sharing of features that can be used in machine-learning models.  \n\n  You can create multiple Feature-Stores within a Snowflake account, for example organized by environment or business unit.  Features are automatically maintained using Snowflake Dynamic Tables to ensure that the feature-engineering pipeline keeps features updated to the required level of freshness. When retrieving features for training or inference, feature-store ensures that the feature values are retrieved at the required point-in-time using Snowflake AsOf joins.\n\n  ![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/snowflake_feature_store.png)\n\n- [Snowpark ML Modeling](https://docs.snowflake.com/en/developer-guide/snowpark-ml/snowpark-ml-modeling), which enables the use of popular Python ML frameworks, such as scikit-learn and XGBoost, for feature pre-processing and model training without the need to move data out of Snowflake.\n\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/snowpark_ml_modeling.png)\n\n- [Snowflake Model Registry](https://docs.snowflake.com/en/developer-guide/snowpark-ml/snowpark-ml-mlops-model-registry), which provides scalable and secure model management of ML models in Snowflake, regardless of origin.\nUsing these features, you can build and operationalize a complete ML workflow, taking advantage of Snowflake's scale and security features.\n\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/snowpark_ml_arch.png)\n\n\n### What You Will Learn\n\n- How to perform data and feature engineering tasks using Snowpark DataFrames and APIs\n- How to create a Snowflake Feature Store, and the key objects within it like Entities and Feature Views\n- How to train ML model using Snowpark ML in Snowflake using features and training data derived from Snowflake Feature Store\n- How to register ML model and use it for inference from Snowflake Model Registry.\n- How to operationalize an end-to-end feature-engineering and ML model to perform micro-batch inference.\n\n### Prerequisites\n\n- [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) installed\n- [Python 3.11](https://www.python.org/downloads/) installed\n  - Note that you will be creating a Python environment with 3.11 in the **Get Started** step\n- A Snowflake account with [Anaconda Packages enabled by ORGADMIN](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#using-third-party-packages-from-anaconda). \n- A Snowflake account login with the ACCOUNTADMIN role. If you have this role in your environment, you may choose to use it. If not, you will need to use a different role that has the ability to create database, schema, tables, dynamic tables, tags,  stages, tasks, user-defined functions, and stored procedures OR 3) Use an existing database and schema in which you have been granted the permissions to create the afore mentioned objects.\n\n\u003E **IMPORTANT**: Before proceeding, make sure you have a Snowflake account with Anaconda packages enabled by ORGADMIN as described [here](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages#getting-started).\n\n\n### What You’ll Build \n- An end-to-end Machine Learning use-case including data-retrieval, feature-engineering and management, model-training and storage and operationalization as a scheduled inference process.\n\n\u003C!-- ------------------------ --\u003E\n## Setup Python\n\n### Get Started\n\n\nThis section covers cloning of the GitHub repository and setting up your Snowpark for Python environment.\n\n### Clone GitHub Repository\n\nThe very first step is to clone the [GitHub repository](https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store). This repository contains all the code you will need to successfully complete this Guide.\n\nUsing HTTPS:\n\n```shell\ngit clone https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store.git\n```\n\nOR, using SSH:\n\n```shell\ngit clone git@github.com:Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store.git\n```\n\n### Snowpark for Python\n\nTo complete the Guide we will need a Python environment installed with the prerequisite packages.\n\n#### Local Python Environment Installation\n\n**Step 1:** Download and install the miniconda installer from [https://conda.io/miniconda.html](https://conda.io/miniconda.html). *(OR, you may use any other Python environment with Python 3.10, for example, [virtualenv](https://virtualenv.pypa.io/en/latest/))*.\n\n**Step 2:** Open a new terminal window and execute the following commands in the same terminal window.\n\n**Step 3:** Create Python 3.10 conda environment called **snowpark-de-ml** with initial required packages by running the following command in the same terminal window\n\n```python\nconda create -n py-snowpark_df_ml_fs python=3.10 numpy pandas pyarrow jupyterlab tabulate --override-channels -c https://repo.anaconda.com/pkgs/snowflake\n```\n\nNote: if you are installing onto a Apple Mac with Apple silicon you will need to use the following instead\n\n```python\nCONDA_SUBDIR=osx-64 conda create -n py-snowpark_df_ml_fs python=3.10 numpy pandas pyarrow jupyterlab tabulate --override-channels -c https://repo.anaconda.com/pkgs/snowflake\n```\n\n**Step 4:** Activate conda environment **snowpark-de-ml** by running the following command in the same terminal window\n\n```python\nconda activate py-snowpark_df_ml_fs\n```\n\nNote: if you are installing onto a Apple Mac with Apple silicon you will need to use the following command after you have activated the Conda environment.\n\n```python\nconda config --env --set subdir osx-64\n```\n\n**Step 5:** Install Snowpark Python, Snowpark ML, and other libraries in conda environment **py-snowpark_df_ml_fs** from [Snowflake Anaconda channel](https://repo.anaconda.com/pkgs/snowflake/) by running the following command in the same terminal window\n\n```python\nconda install -c https://repo.anaconda.com/pkgs/snowflake snowflake-snowpark-python=1.16.0 snowflake-ml-python=1.5.1 notebook\npip install snowflake\n```\n\n\n**Step 6:** Create a Jupyter Kernel to represent the environment that we have just created using\n\n```python\nipython kernel install --user --name=py-snowpark_df_ml_fs\n```\n**Step 7:** Make sure you are in the top level directory for this Guide, and start Jupyter to test the it is setup correctly\n\n```python\njupyter lab\n```\n\nFollow the instructions output by Jupyter in the console to open jupyter lab in your browser, if it has not automatically open a tab in your browser.\n\n**Step 8:** \nYou will need a Snowflake login and to setup a connection for use within the provided Jupyter Notebooks. \n\nThere are several options for creating a Snowpark connection.  You can use the method described here in [snowpark connection](https://docs.snowflake.com/en/developer-guide/snowpark-ml/overview#connecting-to-snowflake).  You will need to create an entry in your [SnowSQL configuration file](https://docs.snowflake.com/en/user-guide/snowsql-config).\n\nAlternatively, you can update [connection.json](https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store/connection.json) with your Snowflake account details and credentials.\n\nHere's a sample ***connection.json*** based on the object names mentioned in **Setup Database Environment** step.\n\n```json\n{\n  \"account\"   : \"\u003Cyour_account_identifier_goes_here\u003E\",\n  \"user\"      : \"\u003Cyour_username_goes_here\u003E\",\n  \"password\"  : \"\u003Cyour_password_goes_here\u003E\",\n  \"role\"      : \"FS_QS_ROLE\",\n  \"warehouse\" : \"TPCXAI_SF0001_WH\",\n  \"database\"  : \"TPCXAI_SF0001_QUICKSTART_INC\",\n  \"schema\"    : \"TRAINING\"\n}\n```\n\nYou can then read the parameters into Python with \n```python\nconnection_parameters = json.load(open('connection.json'))\n```\n\nAnd connect to the database using:\n\n```python\nsession = Session.builder.configs(connection_parameters).create()\n```\n\n\u003E \n\u003E __Note__: For the **account** parameter above, specify your **account identifier** and do not include the snowflakecomputing.com domain name. Snowflake automatically appends this when creating the connection. For more details on that, [refer to the documentation](https://docs.snowflake.com/en/user-guide/admin-account-identifier.html).  \n\n\u003E \n\u003E __Note_: Initially, before you have executed the Setup Notebook below, you will need to use pre-existing objects (database, schema, warehouse) you have access to in your account. Once we have setup the Database environment with the Notebook below you can update `connection.json` with the objects created. \n\n\u003C!-- ------------------------ --\u003E\n## Setup Database Environment\n\n\nWe will now need to setup the Database to mimic development and production databases environments, where new data is being regularly ingested to maintain the latest data from source systems.  Snowflakes Feature Store automates the maintenance of the feature-engineering pipelines from source tables in Snowflake which we want to observe in this Guide.\n\nWe are using the dataset from [TPCX-AI](https://www.tpc.org/tpcx-ai/default5.asp) and it's usecases for this Guide example.  We will use Usecase 1, which performs a customer segmentation using customer Orders, Line-Items and Order-Returns tables.  We will also load the Customer data should you wish to further enrich the use-case with additional customer data.\n\nFor each table TPCX-AI provides three parts to the data:\n* __TRAINING__ (_Development_)\n* __SCORING__ (_Test_)\n* __SERVING__ (_Production_)\n\nWe can think of Training as representing our Development environment where we maintain a sample of historical data that is used for development purposes.  The Scoring environment represents a Test environment where we store data not used in Development to validate our code is working. In Machine Learning this data is used to validate that the features and models we have developed, will generalise well to typical data not observed during the development (training) process.  Finally Serving represents the Production environment where we have new 'live' data arriving from source systems. It is on this new data that we want to operationalize our machine-learning pipeline to perform predictions/inference on new data, taking actions as a result.\n\nWe will create a Snowflake Database (__TPCXAI_SF0001_QUICKSTART__) to hold three schemas (__TRAINING__, __SCORING__, __SERVING__) representing the environments and different data subsets for each table.  This database will contain static source data loaded from Parquet files.  From this we will create another Database (__TPCXAI_SF0001_QUICKSTART_INC__) with the same schemas, and objects, that will incrementally ingest data from __TPCXAI_SF0001_QUICKSTART__ to mimic a 'Live' environment.  We also have the __CONFIG__ schema that holds the Stages, File-Formats, Tasks, Streams etc, that populate and maintain the data in from source and between the two database.\n\nTo simplify the overall creation and setup of these databases and the required objects within them we provide a Jupyter Notebook ([Step01_TPCXAI_UC01_Setup.ipynb](https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store/tree/main/Step01_TPCXAI_UC01_Setup.ipynb))\n\nThe Notebook uses the SnowSQL configuration method of creating the database connection.  If you prefer you can use the `connections.json` method by adjusting this file :\n* `connections.json` : Containing the connection credentials for the Account that you are using. Customise as appropriate to your account.\n\nYou will need to adjust the Notebook to load the file into python:\n\n```python\nconnection_parameters = json.loads('connections.json)\n```\n\nWithin your Jupyter session you should see the Notebook file (`Step01_TPCXAI_UC01_Setup.ipynb`) in the file-browswer.  Open the Notebook, select (top-right) the Conda Environment/Jupyter Kernel (`py-snowpark_df_ml_fs`) that we created earlier for the Notebook.\n\nYou will need to do the same for the other Notebooks used in this Guide\n\nStep through the Notebook running the cells to setup the Database environment.\n\u003E \n\u003E __Note:__ Pay particular attention to the third code cell, and make any adjustments needed for your account/environment and user. See below:\n\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/setup_adjustment_cell.png)\n\n\u003E \n\u003E __Note:__ If you are lacking specific elevated privileges needed for some of the operations, you may encounter issues executing some of the steps.  You will need to work with an Account administrator to resolve these.\n\nOnce you have successfully executed the Notebook, you can check in Snowsight that your environment has been created.  The Database viewer should look something like this:\n\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/Database_Hierarchy_post_setup.png)\n\nYou will find a similar hierarchy under the TPCXAI_SF0001_QUICKSTART_INC\n\nWe can see that a number of Streams & Tasks have been created that are running frequently to incrementally add new data into your __TPCXAI_SF0001_QUICKSTART_INC__ database tables.  We can check that these are running by looking at Snowsight Task History under Monitoring on the left-hand side.  Once it has been running for a while it should something like this.\n\n\u003E \n\u003E __Note:__ If you have only just created the Tasks you will see less execution history.\n\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/task_history.png)\n\nYou can drill into the Task details from the Data viewer.  For example:\n\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/append_scoring_lineitem_task.png)\n\nYou can see that the Tasks are set to execute every minute if new data is available.  Feel free to reduce the frequency should you want to, although you will then need to wait longer to observe changes in FeatureViews when you create them in the Guide.\n\n\u003E \n\u003E __Note:__ If you are going to leave these running for any length of time, assuming you are completing the Guide over a few days. You should also `SUSPEND` the Tasks to minimise costs.  You can `RESUME` them when you want to restart, and they will pick up where they left off, loading any additional data for the interim period.  You can do this via the Elipsis in the top-right corner, or programmatically with SQL if you prefer.\n\n\n\u003C!-- ------------------------ --\u003E\n## Feature Engineering & Model Training\n\n### Key Snowfake Feature Store API Concepts\nIn the Snowflake Feature Store, as typical of other Feature Store solutions:\n* __Entities__ - define the business-entity and the level that we want to gather data and develop ML models at. (e.g. store or/and product key etc). \n* __Features__ are defined and grouped within __FeatureViews__.  In Snowflake Feature Store features are columns, or column-expressions defined via the Snowpark for Python dataframe api, or via SQL directly.\n* __FeatureViews__ are associated (defined) for one or more __Entities__. A __FeatureView__ can be defined with 1:n Entities, but typically only one.\nSeveral (many) __FeatureViews__ may contain Features for the same Entity. FeatureViews tend to get defined based on the data-source they are derived from, the data’s refresh or calculation frequency.  A __FeatureView__ us defined via a Snowpark Dataframe (or SQL expression) enabling a complex pipeline to be used.\n* The __Entity__ (key columns) are used to join __FeatureViews__ together when needed to gather features from multiple __FeatureViews__ within a single training or inference dataset, or derive new __FeatureViews__.\n* A __FeatureSlice__ provides a way of creating a subset of the Features from a single __FeatureViews__ when needed.  It can be used within the API, pretty much anywhere the __FeatureViews__ can be used.\n* __FeatureViews__ and __FeatureSlices__ can be merged (via merge_features) to gather features together and create a new __FeatureView__. For example, all the features for a given __Entity__ could be gathered via the merge into a single. \n\nYou can  learn more about Snowflake Feature Store in this [section](https://docs.snowflake.com/developer-guide/snowpark-ml/feature-store/overview) of the documentation. \n\n### Setting up the Feature Store\nWith our database established, we are now ready to get started on Feature Engineering and Model Training.  Open the Jupyter Notebook ([Step02_TPCXAI_UC01_Feng_and_Train.ipynb](https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store/tree/main/Step02_TPCXAI_UC01_Feng_and_Train.ipynb)) to get started.  Adjust the Notebook connection method if needed for your environment.\n\nYou can step through the Notebook to create Feature Engineering Pipeline, Feature-Store & Model-Registry, interact with the Feature Store and train a model using Snowpark ML.  We will describe some of the key steps in the Notebook below.\n\n\u003E \n\u003E __Note:__ You can now adjust your ***connection.json*** file, to reflect the database, schema and warehouse that you have created in the prior Step.\n\n\u003E \n\u003E __Note:__ As before , pay particular attention to the third code cell, and make any adjustments needed for your account/environment and user. See below:\n\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/setup_adjustment_cell.png)\n\n\nWe use a couple of helper functions `create_FeatureStore` and `create_ModelRegistry` imported from `useful_fns.py` to create our Feature-Store and Model-Registry.  These functions check for the prior creation of these, and create them if they are not already created.  If they are already created they create a python class-instance referencing them.\n\nCreating the Feature Store creates a schema (with the provided name `_TRAINING_FEATURE_STORE` ) in our (__TPCXAI_SF0001_QUICKSTART_INC__) database. This schema contains all the objects created through your interactions with the Python Feature Store API.  Database objects are tagged with Feature Store related tags to denote that they are part of the Feature Store.  These tags are used by Snowsight to discover and present Feature Store objects.  The two main other types of database objects that you will see being created are Dynamic Tables and Views.  We will describe these in more detail later in this section.\n\nThe diagram below depicts the Feature Store information-architecture and how objects in the Python API relate to Database objects.\n\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/fs_information_architecture.png)\n\n### Entity creation\nNow we have our Feature Store created we can create the Entity that we will be using for this use case.  We are building a customer segmentation process, so we will primarily be deriving features at the Customer level.\n\n```python\ncustomer_entity = Entity(name=\"CUSTOMER\", join_keys=[\"O_CUSTOMER_SK\"],desc=\"Primary Key for CUSTOMER\")\nfs.register_entity(customer_entity)\n```\nThe code above defines an instance of the Feature Store entity.  The `register_entity` method creates the object in the database. Entities are created as database tags.  Other Feature Store objects that are created that relate to this Entity are tagged with this tag as we will see shortly.\n\nWe can `list_entities()` which returns a Snowpark dataframe that can be `.show()` or filtered as needed.  We can also provide SQL wild-card expressions within `list_entities()` for filtering by name elements.\n\n### Feature Engineering Pipeline\nFeature engineering pipelines are defined using Snowpark dataframes (or SQL expressions).  In the `feature_engineering_fns.py` file We have created two feature engineering functions to create our pipeline :\n* __uc01_load_data__(order_data: DataFrame, lineitem_data: DataFrame, order_returns_data: DataFrame) -\u003E DataFrame   \n* __uc01_pre_process__(data: DataFrame) -\u003E DataFrame\n\n`uc01_load_data`, takes the source tables, as dataframe objects, and joins them together, performing some data-cleansing by replacing NA's with default values. It returns a dataframe as it's output.\n\n`uc01_pre_process`, takes the dataframe output from `uc01_load_data`  and performs aggregation on it to derive some features that will be used in our segmentation model.  It returns a dataframe as output, which we will use to provide the feature-pipeline definition within our FeatureView.\n\nIn this way we can build up a complex pipeline step-by-step and use it to derive a FeatureView, that will be maintained as a pipeline in Snowflake.\n\n### FeatureView Creation\nWe will use the dataframe that we defined in the prior step for the FeatureView we are creating. The FeatureView will create a Dynamic Table in our Feature Store schema.  We could use the dataframe directly within the definition of the FeatureView.  The SQL query generated from Snowpark through the dataframe definition, is machine generated and not necessarily easy for a human to parse, when used and viewed within the Dynamic Table.  Therefore optionally we can parse the SQL and format it to something more human readable.  We use the `sqlglot` Python package to do this.  We created a simple function that takes the raw SQL generated from Snowpark, parses it and returns a formatted SQL statement. Depending on your preference, you can choose to convert sub-selects to common-table-expressions.  \n\nThe image below shows the FeatureView creation process, and calls out a few key elements of the FeatureView definition.\n\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/fv_creation.png)\n\nSimilarly to the Entity creation,  this is a two step process, first creating the python instance, and then registering the instance to create an object in the database.  We provide the feature view name, version, description, and individual descriptions for each feature. We can create new versions of a Feature as it evolves, for example if the definition of some of the Features within change. Once created a version is immutable, unless a forced replacement is needed and invoked via `overwrite = True`. \n\nWe add the Entity (`CUSTOMER`) that we created earlier. This allows the relationship, and join keys, available in the Feature View to be defined.  We will see how this is used when we want to retrieve Features from the feature store. \n\nIf we provide `refresh_freq` [optional argument] the database object that is created from the Feature View definition is a Dynamic Table, otherwise a View is created.  In the case of a Dynamic Table, the table is initially populated with data, and from that point forward incrementally maintained when new data lands in the source tables. As we have created incrementing data sources, we can observe this incremental processing being applied to the table, using Snowsight's Dynamic Table observation features.  See the image below.\n\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/fv_dynamic_table.png)\n\nThe Snowsight UI also contains a new section supporting Feature Store discovery and observability. can be used to search, discover and review available Features for a given machine-learning task, enabling re-use of features across multiple models, and expediting the time required to implement machine-learning projects. The below image shows the Snowsight UI Feature Store section, Entity level view.  We can see the FeatureView that we have created, under the Customer Entity.  We can also see other Entities, and FeatureViews that have been created for other use-cases within this Feature Store.\n\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/FeatureStore_Snowsight.png)\n\nThe feature-store provides lineage of data from source tables, through feature-engineering to model and model-inference enabling users to understand the broader impact in data-quality issues in source data, answering questions like:\n  * what features and models are derived from this source/table.\n  * what data-engineering and transformations are applied to derive this feature.\n\n### Feature Retrieval\nNow we have a Feature View with data being maintained within it, we can use it to retrieve data for model-training, and model-inference.  The Feature Store enables feature-values to be retrieved for a given set of Entity-keys, relative to a reference point-in-time.  Under the covers the Feature Store uses the new SQL AsOf join functionality in Snowflake to efficiently retrieve the requested features across the FeatureViews.  The Entity-Keys and Timestamps are provided as a dataframe, which we call a Spine.  The Spine can be defined using Snowpark Dataframe funcionality, or via a SQL expression.\n\nFor example, we can create the spine with the following:\n```python\nspine_sdf =  fv_uc01_preprocess.feature_df.group_by('O_CUSTOMER_SK').agg( F.max('LATEST_ORDER_DATE').as_('ASOF_DATE')\n```\n\nWe can then use the Spine to create a Dataset.  Datasets are a new type of data-object in Snowflake that allows immutable datasets that are optimised for Machine Learning to be persisted and read directly into common machine learning frameworks like scikit-learn, Tensorflow and Pytorch.  We create the Dataset with the following:\n```python\ntraining_dataset = fs.generate_dataset( name = 'UC01_TRAINING',\n                                        spine_df = spine_sdf, features = [fv_uc01_preprocess], \n                                        spine_timestamp_col = 'ASOF_DATE'\n                                        )  \n```\nThe Dataset can also be converted into other object types if needed.  For example, we can create a Snowpark Dataframe or a Pandas dataframe from the Dataset with the following code.\n```python\n# Snowpark Dataframe\ntraining_dataset_sdf = training_dataset.read.to_snowpark_dataframe()\n# Pandas Dataframe\ntraining_dataset_pdf = training_dataset.read.to_pandas()\n```\n\n### Fit a Snowpark-ML Kmeans Model\nWe use the training Dataset we created in the previous step to fit a Snowpark-ML Kmeans model. You can read more about Snowpark ML Model Development in this [section](https://docs.snowflake.com/developer-guide/snowpark-ml/modeling) of the documentation. To do so we define our model fitting pipeline as a function that includes some feature pre-processing to scale our input variables using min-max scaling.  These transformations need to be applied at model time, as they capture the global state (e.g. minimum and maximum values for columns) of our training sample.\n\nWe fit the model and log it to the Model Registry that we created earlier. You can read more about Snowflake ML Model Registry in this [section](https://docs.snowflake.com/en/developer-guide/snowpark-ml/model-registry/overview) of the documentation. As with the Snowflake Feature Store, models in the registry are versioned. When we fit our model with Snowpark ML, using the Feature Store and register the model in the Registry, Snowflake captures the full lineage from source tables through to the model. We can interogate the lineage information to understand what models might be impacted by a data-quality problem in our source tables for example.\n\nModel fitting and optimisation is typically a highly iterative process where different subsets of features, over varying data samples are used in combination with different sets of model hyper-parameters.  With feature store and model lineage and Model Registry all the the information related to each fitting run is captured, so that we have full Model Reproducibility and Discovery should we need. During this process we would normally check our model against a test dataset, to generate test-scores for the model.   Many more sophisticated  validation techniques exist, but are beyond the scope of this Guide. \n\n* Within-Cluster Sum of Squares\n* Silhouette Score\n* Gap Statistics\n* Cross-Validation\n\nIn the Notebook we have simply plotted the clusters to review visually.\n\n![snowpark_ml](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/advanced-guide-to-snowflake-feature-store/cluster_plot.png)\n\nThis ends the model-development phase.  From this point on, we assume that the simple model we created is good enough for production and operationalization. \n\n\u003C!-- ------------------------ --\u003E\n## Model Operationalization in Production\n\nWe will use a new Notebook ([Step03_TPCXAI_UC01_Operations.ipynb](https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store/tree/main/Step03_TPCXAI_UC01_Operations.ipynb)) for the Model Operationalisation stage.  This may be created by a different person/role in the organisation. For example a data or ML engineer.  Open the Notebook and adjust the Notebook connection method if needed for your environment.\n\nThis notebook shows how you can easily replicate the training feature-engineering pipeline, created during model development, in the SERVING (_Production_) schema. We then create an inference function and deploy a new FeatureView that schedules ongoing inference from new data flowing through our Feature Engineering pipeline from our source data.  We can monitor the production pipeline (Dynamic Tables) using the same tools that we have already seen in the Feature Engineering and Model Training phase.\n\n### Recreate the Feature-Engineering pipeline\nWe created FeatureViews in our `_TRAINING_FEATURE_STORE` (Development) schema.  We will create another Feature Store (`_SERVING_FEATURE_STORE`) for the Production environment. This will hold new FeatureViews created with the same definition, but running over Production data.  We can easily modify the tables that are referenced in production, versus development, by changing the Schema in the dataframe definition. We assume that the database tables are defined identically between development and production.\n\nFor this Guide we have chosen to share the Model-Registry across all environments as we will use the model we trained in Development, in Production for inference.  Alternatively, we could also create a new seperate Model Registry for production and Copy models between environments, or retrain the Model in production with appropriate checks and balances to ensure the new model over production data is still good for operationalisation.\n\nWhen we register our model in the Model Registry it packages it as a Python function which enables direct access from [Python](https://docs.snowflake.com/developer-guide/snowpark-ml/model-registry/overview#calling-model-methods) or from [SQL](https://docs.snowflake.com/sql-reference/commands-model#label-snowpark-model-registry-model-methods).  This allows the creation of an inference Feature View that uses the model directly for prediction from our Feature Engineering pipeline, \n\n### Create an Inference Feature view\nWe define our model inference function, which we pass our feature values and model into.\n```python\ndef uc01_serve(featurevector, km4_purchases) -\u003E DataFrame:\n    return km4_purchases.run(featurevector, function_name=\"predict\")\n```\n\nWe define a dataframe that reads all the records from our feature engineering pipeline. When used within the FeatureView, the Dynamic Table that gets created, will incrementally process change data once the initial Dynamic Table has been created. \n```python\ninference_input_sdf = fs.read_feature_view(fv_uc01_preprocess)\n```\n\nWe then create a FeatureView that will compute Inference on incremental data in the feature engineering pipeline, keeping an up to date set of customer segments through time.  \n```python\n## Create & Register Inference-FeatureView to run scheduled Inference\ninf_fvname = \"FV_UC01_INFERENCE_RESULT\"\ninf_fv_version = \"V_1\"\n\ninference_features_desc = { \"FREQUENCY\":\"Average yearly order frequency\",\n                              \"RETURN_RATIO\":\"Average of, Per Order Returns Ratio.  Per order returns ratio : total returns value / total order value\", \n                              \"RETURN_RATIO_MMS\":f\"Min/Max Scaled version of RETURN_RATIO using Model Registry ({tpcxai_database}_MODEL_REGISTRY) Model ({mv.model_name}) Model-Version({mv.version_name}) Model Comment ({mv.comment})\",\n                              \"FREQUENCY_MMS\":f\"Min/Max Scaled version of FREQUENCY using Model Registry ({tpcxai_database}_MODEL_REGISTRY) Model ({mv.model_name}) Model-Version({mv.version_name})  Model Comment ({mv.comment}\",\n                              \"CLUSTER\":f\"Kmeans Cluster for Customer Clustering Model (UC01) using Model Registry ({tpcxai_database}_MODEL_REGISTRY) Model ({mv.model_name}) Model-Version({mv.version_name})  Model Comment ({mv.comment}\"}\n\ntry:\n   fv_uc01_inference_result = fs.get_feature_view(name= inf_fvname, version= inf_fv_version)\nexcept:\n   fv_uc01_inference_result = FeatureView(\n         name= inf_fvname, \n         entities=[customer_entity], \n         feature_df=inference_result_sdf,\n         refresh_freq=\"60 minute\",  # \u003C- specifying optional refresh_freq creates FeatureView as Dynamic Table, else created as View.         \n         desc=\"Inference Result from kmeans model for Use Case 01\").attach_feature_desc(inference_features_desc)\n   \n   fv_uc01_inference_result = fs.register_feature_view(\n         feature_view=fv_uc01_inference_result, \n         version= inf_fv_version, \n         block=True\n   )\n   print(f\"Inference Feature View : fv_uc01_inference_result_{inf_fv_version} created\")   \nelse:\n   print(f\"Inference Feature View : fv_uc01_inference_result_{inf_fv_version} already created\")\nfinally:\n   fs_serving_fviews = fs.list_feature_views().filter(F.col(\"NAME\") == inf_fvname ).sort(F.col(\"VERSION\").desc())\n   fs_serving_fviews.show() \n```\n\nIn the FeatureView definition we have embellished our feature comments with the model name and model-version to make it directly available in the database object definition, but this information can also be derived through the feature and model registry lineage api.\n\nOnce we have created the FeatureView we can retrieve inferences from it.\n\n```python\nfv_uc01_inference_result.feature_df.sort(F.col(\"LATEST_ORDER_DATE\").desc()).show(100)\n```\nWe can monitor how CUSTOMERS behaviour (segment) changes over time and take targetted action as a result.\n\n\n\u003C!-- ------------------------ --\u003E\n## Clean Up\n\nOnce you have completed this Guide and no longer need the databases and objects created by it you will want to clean up.  We provide a Notebook that does this. [Step04_TPCXAI_UC01_Cleanup.ipynb](https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store/tree/main/Step04_TPCXAI_UC01_Cleanup.ipynb)\n\nIf you want to keep the data, but shut down the Tasks and Dynamic Tables to minimise compute cost, you will need to go to each Task and Dynamic Table to `SUSPEND` them.  This can be done in the Snowsight UI, or you can use the applicable SQL commands to achieve the same.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\n\nCongratulations! You've successfully performed Feature Engineering using Snowpark, made use of Snowflake Feature Store to publish and maintain features in a development and production environment. You've learnt how you can deploy a model from the Snowflake Model Registry and combine it with a feature-engineering pipeline in Feature Store to operationalise an incremental inference process in Snowflake ML.\n\nWe would love your feedback on this Guide! Please submit your feedback using this [Feedback Form](https://forms.gle/JeZWYwkCMk3gty7D7).\n\n### What You Learned\n\n- How to analyze data and perform data engineering tasks using Snowpark DataFrames and APIs\n- How to use open-source Python libraries from curated Snowflake Anaconda channel\n- How to create Snowflake Tasks to automate data pipelines\n- How to train ML model using Snowpark ML in Snowflake\n- How to register ML model and use it for inference from Snowflake Model Registry\n- How to create Streamlit application that uses the ML Model for inference based on user input\n\n### Related Resources\n\n- [Source Code on GitHub](https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-feature-store)\n- [Intro to Machine Learning with Snowpark ML](/en/developers/guides/intro-to-machine-learning-with-snowpark-ml-for-python/)\n- [Advanced: Snowpark for Python Data Engineering Guide](/en/developers/guides/data-engineering-pipelines-with-snowpark-python/)\n- [Advanced: Snowpark for Python Machine Learning Guide](/en/developers/guides/getting-started-snowpark-machine-learning/)\n- [Snowpark for Python Developer Guide](https://docs.snowflake.com/en/developer-guide/snowpark/python/index.html)\n- [Snowpark for Python API Reference](https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/index.html)\n- [Snowflake Feature Store](https://docs.snowflake.com/developer-guide/snowpark-ml/feature-store/overview)\n- [Snowpark ML Modelling](https://docs.snowflake.com/developer-guide/snowpark-ml/modeling)\n- [Snowflake Model Registry](https://docs.snowflake.com/en/developer-guide/snowpark-ml/model-registry/overview)\n\n\n\u003C!-- ------------------------ --\u003E\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-674548bdcf","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-f11524130e",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-d5ea67de05","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-903962d1f0","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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