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Repo"}},"flexible_column_cont":{"id":"flexible-column-container-7b52a10cb6","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-ac6deab83e",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-2083957b76","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThis Quickstart guide is the second in a series of Collaboration & Applications in Snowflake. Part One of the series can be found \u003Ca href=\"/en/developers/guides/data-collaboration-two-way/\"\u003Ehere\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003EIn Part One, we showed how using Snowflake's unique collaboration features, we could:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Eshare data from an organisation to another\u003C/li\u003E\u003Cli\u003Escore that data using an ML model\u003C/li\u003E\u003Cli\u003Eshare it back with the original provider\nall in an automated pipeline\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIn this Quickstart, we will show how we can leverage the Native Apps Framework to simplify this process even further.\u003C/p\u003E\n","\u003Cp\u003EThe Native App Framework is a collaboration framework that allows providers to share data and/or related business logic to other Snowflake customers. In the previous Quickstart guide, we set up a bi-directional share between two organisations. With the Native App framework, we can simplify this relationship by allowing one party to share its logic in the form of an application to the other. Therefore, we have brought the app to the data, as opposed to bringing the data to the app\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-collaboration-native-app/native_app_overview.png\" alt=\"Diagram\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThere are numerous benefits to leveraging the Native App framework for our Use Case. For example:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EData Sovereignty and Security for the Consumer. In the previous quickstart, it was necessary for the provider to share data with another entity. This could be potentially sensitive information, and therefore a bigger hurdle for security and governance teams to approve. With Native Apps, the app logic is brought to the consumers dataset, so no customer data is leaving their security perimeter\u003C/li\u003E\u003Cli\u003ECompliance Risk Reduction for the Provider. Similar to the above, the provider of the model may not want to have access to customer data. This requires extra consideration with how to securely handle this data, which is not necessary if the model is shared to their customers to run against their own data.\u003C/li\u003E\u003Cli\u003EIncreased Margin. By sharing the model to the consumer, the infrastructure cost to execute the model are now with the consumer. This means the provider can monetise the model, without worrying about infrastructure costs. Similarly the consumer can set warehouse sizes appropriate to the SLAs and budgets of the consumer organisation.\u003C/li\u003E\u003Cli\u003EFaster Onboarding. By having a replicable model, the provider can simply leverage the Snowflake Marketplace for distribution. They no longer need to go through a lengthy process of approvals to set up a data pipeline with their customers.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to train an ML model in Snowflake\u003C/li\u003E\u003Cli\u003EHow to package the ML Model in an Application Package\u003C/li\u003E\u003Cli\u003EHow to privately list a Native Application\u003C/li\u003E\u003Cli\u003EHow to Consume a Native Application\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Native Application that contains an ML Model shared via a Private Listing\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe architecture of what we will build is shown below\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-collaboration-native-app/model_na_architecture.png\" alt=\"Diagram\"\u003E\u003C/p\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\n\u003Cstrong\u003EDownload the git repo here: https://github.com/Snowflake-Labs/sfguide-data-collaboration-native-app\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.anaconda.com/\"\u003EAnaconda\u003C/a\u003E installed\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.python.org/downloads/\"\u003EPython 3.10\u003C/a\u003E installed\n\u003Cul\u003E\u003Cli\u003ENote that you will be creating a Python environment with 3.10 in the \u003Cstrong\u003EConsumer Account - Create Model\u003C/strong\u003E step\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003ESnowflake accounts 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. If you do not have a Snowflake account, you can register for a \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003Efree trial account\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003ESnowflake accounts with the Snowflake Marketplace T&C's accepted. This will allow you to create Listings.\u003C/li\u003E\u003Cli\u003EA Snowflake account login with a role that has the ability to create Listings, Databases, Schemas, Tables, Stages, User-Defined Functions, and Stored Procedures. If not, you will need to register for free trials or use a different role.\u003C/li\u003E\u003Cli\u003EThe \u003Ca href=\"https://docs.snowflake.com/developer-guide/snowpark-ml/index#installing-snowpark-ml-from-the-snowflake-conda-channel\"\u003ESnowpark ML\u003C/a\u003E package installed\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBusiness Use Case and Context\u003C/h2\u003E\n","\u003Cp\u003EThe Business Use case follows a similar scenario as Part One.\u003C/p\u003E\n","\u003Cp\u003EIn this guide, we are playing the role of Zamboni and Snowbank. The Credit Risk team at Snowbank has noticed a rise in credit card default rates which affects the bottom line of the business. Previously, it has shared data with an external organisation (Zamboni) who assist with analysing the data and scoring which customers are most likely to default.\u003C/p\u003E\n","\u003Cp\u003EThis time, the compliance team has said that it is too risky to share this customer data with an external party without proper procedures being followed, which could take a few months to complete. Zamboni have proposed a solution that utilises the Native Application Framework.\u003C/p\u003E\n","\u003Cp\u003ESince both companies use Snowflake, Zamboni has proposed sharing their proprietary Credit Default Scoring Model via the Native Application Framework. The advantages of doing this for Snowbank are:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENo customer data leaves Snowbank's Snowflake Account\u003C/li\u003E\u003Cli\u003EThe Native App runs within the Snowbank Account. Since Snowflake has been approved for use internally, it is not subject to longer onboarding and cybersecurity checks\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe advantages for Zamboni are:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESimple deployment. Zamboni can simply share the model using a similar listing paradigm as sharing a dataset.\u003C/li\u003E\u003Cli\u003EReduced compliance risk. Zamboni's security and compliance team are similarly pleased in being able to satisfy the business needs without needing Snowbank's data hosted on their servers\u003C/li\u003E\u003Cli\u003EIncreased Margin. Zamboni are now able to share their proprietary logic safely, without having to provision infrastructure to run the model on behalf of Snowbank.\u003C/li\u003E\u003Cli\u003ESpeed to Market. Zamboni can simply distribute the model via the Native App framework in a private listing. They no longer need to set up data pipelines. They can also build once, and distribute to lots of different customers by leveraging Snowflake's multitenancy framework.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWhat is different in this scenario compared to Part One, is that it is no longer a bi-directional share of data. Now Zamboni becomes the provider of an Application, and Snowbank becomes the consumer.\u003C/p\u003E\n","\u003Ch3\u003EDataset Details\u003C/h3\u003E\n","\u003Cp\u003EWe will use a publicly available dataset to train our ML model for the purposes of this demo. The dataset contains aggregated profile features for each customer at each statement date. Features are anonymized and normalized, and fall into the following general categories:\u003C/p\u003E\n","\u003Cp\u003ED_* = Delinquency variables\nS_* = Spend variables\nP_* = Payment variables\nB_* = Balance variables\nR_* = Risk variables\u003C/p\u003E\n","\u003Ch3\u003EDataset Citation\u003C/h3\u003E\n","\u003Cp\u003EAddison Howard, AritraAmex, Di Xu, Hossein Vashani, inversion, Negin, Sohier Dane. (2022). American Express - Default Prediction. Kaggle. https://kaggle.com/competitions/amex-default-prediction\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003ENavigate to the \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake Trial Landing Page\u003C/a\u003E. Follow the prompts to create a Snowflake Account. You should receive an email to activate your trial account.\u003C/p\u003E\n","\u003Cp\u003ENavigate to a new worksheet and execute the following commands to create a second account in the Organisation. The second account will be the account we share the model to (Snowbank). In trial accounts we cannot share the Native App externally, although the same process can be followed to share the app externally if you have a non-trial account.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUSE ROLE ORGADMIN;\n\nCREATE ACCOUNT SNOWBANK\n  ADMIN_NAME = ADMIN\n  ADMIN_PASSWORD = 'ENTER PASSWORD HERE'\n  EMAIL = 'ENTER YOUR EMAIL HERE'\n  EDITION = ENTERPRISE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote down the account locator and the url from the output of the command above for use in later steps\u003C/p\u003E\n","\u003Ch3\u003EProvider Account (Zamboni)\u003C/h3\u003E\n","\u003Cp\u003EIn this part of the lab we'll set up our Provider Snowflake account. In our business scenario, this step represents Zamboni developing their proprietary Credit Card Default model from their own datasets. Stay in the same account from the previous section.\u003C/p\u003E\n","\u003Ch3\u003EInitial Set Up\u003C/h3\u003E\n","\u003Cp\u003ENext, open up a new worksheet and run all following steps as the ACCOUNTADMIN role\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003E  -- Change role to accountadmin\n  USE ROLE ACCOUNTADMIN;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext we create two \u003Ca href=\"https://docs.snowflake.com/en/user-guide/warehouses-overview\"\u003EVirtual Warehouses\u003C/a\u003E that can be used to load the initial dataset, and do our model training respectively.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003E-- Create a virtual warehouse for loading and ad-hoc queries\nCREATE OR REPLACE WAREHOUSE QUERY_WH WITH \n  WAREHOUSE_SIZE = 'X-SMALL' \n  WAREHOUSE_TYPE = 'STANDARD' \n  AUTO_SUSPEND = 300 \n  AUTO_RESUME = TRUE \n  MIN_CLUSTER_COUNT = 1 \n  MAX_CLUSTER_COUNT = 1;\n\n-- Create a virtual warehouse for training\nCREATE OR REPLACE WAREHOUSE training_wh with \n  WAREHOUSE_SIZE = 'MEDIUM' \n  WAREHOUSE_TYPE = 'snowpark-optimized' \n  MAX_CLUSTER_COUNT = 1\n  AUTO_SUSPEND = 300 \n  AUTO_RESUME = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ELoad Data\u003C/h3\u003E\n","\u003Cp\u003ENext we will create a database and schema that will house the tables to train our model.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003E-- Create the application database and schema\nCREATE OR REPLACE DATABASE NATIVE_APP_DEMO;\nCREATE OR REPLACE SCHEMA NATIVE_APP_DEMO;\nUSE SCHEMA NATIVE_APP_DEMO.NATIVE_APP_DEMO;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOur data is in Parquet format, so we will create a file format object\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECREATE OR REPLACE FILE FORMAT parquet_format\nTYPE = PARQUET;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext we set up our \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-s3-create-stage#external-stages\"\u003EExternal Stage\u003C/a\u003E to our data. In our business scenario, we would have a secure \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-storage-integration\"\u003EStorage Integration\u003C/a\u003E to the external stage rather than a public s3 bucket.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECREATE OR REPLACE STAGE quickstart_cc_default_training_data\n    URL = 's3://sfquickstarts/two_way_data_share/train/'\n    FILE_FORMAT = parquet_format;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis DDL will create the structure for the table which is the main source of data for our lab.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECREATE OR REPLACE TABLE cc_default_training_data\n  USING TEMPLATE (\n    SELECT ARRAY_AGG(OBJECT_CONSTRUCT(*))\n      FROM TABLE(\n        INFER_SCHEMA(\n          LOCATION=>'@quickstart_cc_default_training_data',\n          FILE_FORMAT=>'parquet_format'\n        )\n      ));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow we will load the data in the tables. We can scale up the warehouse temporarily so we do not have to wait as long\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EALTER WAREHOUSE query_wh SET warehouse_size=MEDIUM;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe code below loads the data in to the tables.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECOPY INTO cc_default_training_data \n  FROM @quickstart_cc_default_training_data \n  FILE_FORMAT = (FORMAT_NAME= 'parquet_format') \n  MATCH_BY_COLUMN_NAME=CASE_INSENSITIVE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe can now scale down the warehouse since we have finished loading data for the lab\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EALTER WAREHOUSE query_wh SET warehouse_size=XSMALL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should have loaded over 5.5 million rows in a few minutes. To check, query the data in the worksheet\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ESELECT COUNT(*) \n  FROM CC_DEFAULT_TRAINING_DATA;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe have loaded all the data in Zamboni. We can now proceed with training the model\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Model\u003C/h2\u003E\n","\u003Ch3\u003EProvider Account (Zamboni)\u003C/h3\u003E\n","\u003Cp\u003EFor this section, make sure you download the corresponding \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-model-sharing-using-native-app\"\u003Egit repo\u003C/a\u003E so you have the files referenced in this section.\u003C/p\u003E\n","\u003Ch3\u003ESet Up Snowpark for Python and Snowpark ML\u003C/h3\u003E\n","\u003Cp\u003EThe first step is to set up the python environment to develop our model. To do this:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EDownload and install the miniconda installer from \u003Ca href=\"https://conda.io/miniconda.html\"\u003Ehttps://conda.io/miniconda.html\u003C/a\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/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOpen a new terminal window in the downloaded directory with the conda_env.yml file and execute the following commands in the same terminal window:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECreate the conda environment.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003Econda env create -f conda_env.yml\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003EActivate the conda environment.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003Econda activate data-collaboration-native-app\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EStart notebook server:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003Ejupyter notebook\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOpen the jupyter notebook Credit Card Default Native App Notebook\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUpdate connection.json with your Snowflake Account Identifier details and User Name in a code editor of your choice.\nHere's a sample based on the object names we created in the last step:\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003E{\n  "account"        : "<your_account_identifier_goes_here>",\n  "user"           : "<your_username_goes_here>",\n  "password"       : "<your_password_goes_here>",\n  "role"           : "ACCOUNTADMIN",\n  "warehouse"      : "QUERY_WH",\n  "database"       : "NATIVE_APP_DEMO",\n  "schema"         : "NATIVE_APP_DEMO"\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can find your account identifier by running the following SQL\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ESELECT CURRENT_ORGANIZATION_NAME() || '-' || CURRENT_ACCOUNT_NAME();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETrain and Register Model\u003C/h3\u003E\n","\u003Cp\u003EOpen up the Credit Card Default Native App notebook and follow the steps. Once you have completed those, you will have trained and deployed a ML Model in Snowflake that predicts credit card default risk. You will also have registered the model in the Snowflake Model Registry.\u003C/p\u003E\n","\u003Cp\u003EStay in the Zamboni account for the next step.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild Native App\u003C/h2\u003E\n","\u003Ch3\u003EProvider Account (Zamboni)\u003C/h3\u003E\n","\u003Cp\u003ENow we have trained the model, we want to encapsulate it in an Application Package, so we can distribute it via the Native App Framework. More information on the Native Application framework can be found in the documentation \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/native-apps/native-apps-about\"\u003Ehere\u003C/a\u003E. We will be working through the \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/native-apps/native-apps-workflow#development-workflow\"\u003ENative App Development Workflow\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EContinue to work through the python notebook. The SQL commands below can be run from the python notebook.\u003C/p\u003E\n","\u003Cp\u003EThe next step is to create an Application Package object that will hold the assets we wish to distribute to Snowbank. An Application Package encapsulates the data content, application logic, metadata, and setup script required by an application. An Application Package also contains information about versions and patch levels defined for the application. We do this by running the following SQL.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EGRANT CREATE APPLICATION PACKAGE ON ACCOUNT TO ROLE ACCOUNTADMIN;\nCREATE APPLICATION PACKAGE IF NOT EXISTS CREDIT_CARD_PREDICTION_APP_PACKAGE;\nSHOW APPLICATION PACKAGES;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext, we need to create a stage within the Application Package for us to put our model assets. We run the following SQL commands\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUSE APPLICATION PACKAGE CREDIT_CARD_PREDICTION_PACKAGE;\nCREATE SCHEMA IF NOT EXISTS MODEL_ASSETS;\nCREATE OR REPLACE STAGE CREDIT_CARD_PREDICTION_APP_PACKAGE.MODEL_ASSETS.MODEL_STAGE FILE_FORMAT = (TYPE = 'csv' FIELD_DELIMITER = '|' SKIP_HEADER = 1);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESo we share a model that is consistent with what is in the model registry, we are going to export the model from the registry, and put it in the Application Package. We do this in the following code block\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Retrieve the model from the registry\nm = registry.get_model("NATIVE_APP_DEMO.NATIVE_APP_DEMO.CREDIT_CARD_DEFAULT_MODEL")\nmv = m.version("default")\n\n# Create a temp directory to export the model\nimport tempfile, pathlib, shutil, os\nlocal_dir = pathlib.Path(tempfile.mkdtemp(prefix="model_export_"))\n\n# export the full artefact set into that folder\nmv.export(local_dir.as_posix(), export_mode=ExportMode.FULL)\n\n# Upload the files and folder structure to the Application Package Stage\nstage_root = "@CREDIT_CARD_PREDICTION_PACKAGE.MODEL_ASSETS.MODEL_STAGE/models"\n\nfor file_path in pathlib.Path(local_dir).rglob('*'):\n    if file_path.is_file():\n        rel_path = file_path.relative_to(local_dir).as_posix()   # e.g. runtimes/env.yml or manifest.yml\n        subdir   = os.path.dirname(rel_path)                     # '' or 'runtimes'\n\n        # remote prefix **must end with a slash** and contain no file-name\n        remote_prefix = f"{stage_root}/{subdir}/" if subdir else f"{stage_root}/"\n\n        session.file.put(\n            file_path.as_posix(),\n            remote_prefix,\n            overwrite=True,\n            auto_compress=False\n        )\n\n# Check that all our artefacts have been uploaded\nsession.sql("LIST @CREDIT_CARD_PREDICTION_PACKAGE.MODEL_ASSETS.MODEL_STAGE/models;").collect()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAn application requires a manifest file, and a setup script (as outlined in the Native App Development Workflow). We upload them to our Application Package in the following commands\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EPUT file://scripts/setup.sql @CREDIT_CARD_PREDICTION_APP_PACKAGE.MODEL_ASSETS.MODEL_STAGE/scripts overwrite=true auto_compress=false;\n\nPUT file://manifest.yml @CREDIT_CARD_PREDICTION_APP_PACKAGE.MODEL_ASSETS.MODEL_STAGE overwrite=true auto_compress=false;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe setup script contains the SQL statements that define the components created when a consumer installs your application. For us, this will include creating stored procedures to perform feature engineering, as well as registering our model for the app to use on the consumer side (documentation \u003Ca href=\"https://docs.snowflake.com/LIMITEDACCESS/native-apps/models\"\u003Ehere\u003C/a\u003E). This is done in the following lines that were run as part of setup.sql on installation of the app:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003Ecreate or replace model APP_CODE.CREDIT_CARD_DEFAULT_MODEL from @CREDIT_CARD_PREDICTION_PACKAGE.MODEL_ASSETS.MODEL_STAGE/models/;\n\ngrant usage on model APP_CODE.CREDIT_CARD_DEFAULT_MODEL to application role app_user;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPerform Local Testing\u003C/h2\u003E\n","\u003Ch3\u003EProvider Account (Zamboni)\u003C/h3\u003E\n","\u003Cp\u003ENow the Application has been created from the Application Package in our Provider Account, we can perform some testing to ensure it behaves as expected. In this Quickstart, we have exposed more views and functions than what is necessary, so we can follow along and see how the app in functioning.\u003C/p\u003E\n","\u003Cp\u003EYou will need a SQL worksheet for this part of the Quickstart. We can load some testing data by executing the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUSE SCHEMA NATIVE_APP_DEMO.NATIVE_APP_DEMO;\n\nCREATE OR REPLACE STAGE quickstart_cc_default_unscored_data\n    URL = 's3://sfquickstarts/two_way_data_share/unscored/'\n    FILE_FORMAT = parquet_format;\n\n-- Create below to be the template of the above (without the target column)\nCREATE OR REPLACE TABLE cc_default_unscored_data LIKE cc_default_training_data;\nALTER TABLE cc_default_unscored_data DROP COLUMN "target";\n\nALTER WAREHOUSE query_wh SET warehouse_size=MEDIUM;\n\nUSE WAREHOUSE query_wh;\n\nCOPY INTO cc_default_unscored_data \n  FROM @quickstart_cc_default_unscored_data \n  FILE_FORMAT = (FORMAT_NAME= 'parquet_format') \n  MATCH_BY_COLUMN_NAME=CASE_INSENSITIVE;\n\nALTER WAREHOUSE query_wh SET warehouse_size=XSMALL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis app requires the consumer to explicitly provide a reference to a table in the Snowflake instance it is installed as an input. References are a way for Native App consumers to be explicit in their permissioning to any existing object in a Snowflake account that is required. This ensures that Native Applications run as "isolated objects" within a Consumer Account, and must be granted explicit permission to access other objects. More information can be foind \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/native-apps/requesting-refs\"\u003Ehere\u003C/a\u003E. We can achieve referencing through the UI, or running the following SQL.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003E-- Bind the reference to a table in our own Snowflake Account. This can be done via SQL or the UI\nCALL credit_card_prediction_app.app_code.register_single_reference('RAW_TABLE' , 'ADD', SYSTEM$REFERENCE('TABLE', 'NATIVE_APP_DEMO.NATIVE_APP_DEMO.CC_DEFAULT_UNSCORED_DATA', 'PERSISTENT', 'SELECT'));\n\n-- Check that the reference worked\nSHOW REFERENCES IN APPLICATION credit_card_prediction_app;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext let's run through the commands and see if they are working as expected. Note that we are running code inside an isolated Application object. This application object will eventually be shared with our consumer (Snowbank).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003E-- This should be empty, as we have not called the procedure to populate the table yet\nSELECT * FROM CREDIT_CARD_PREDICTION_APP.APP_CODE.RAW_TABLE_VIEW;\n\n-- Now we call the procedure\nCALL CREDIT_CARD_PREDICTION_APP.APP_CODE.CREATE_TABLE_FROM_REFERENCE_RAW();\n\n-- And the table should now be populated\nSELECT * FROM CREDIT_CARD_PREDICTION_APP.APP_CODE.RAW_TABLE_VIEW LIMIT 10;\nSELECT COUNT(*) FROM CREDIT_CARD_PREDICTION_APP.APP_CODE.RAW_TABLE_VIEW;\n\n-- Next we call the procedure to do the feature engineering. This will take 5 mins to complete\nCALL CREDIT_CARD_PREDICTION_APP.APP_CODE.CC_PROFILE_PROCESSING();\n\n-- The feature table should now be populated\nSELECT * FROM CREDIT_CARD_PREDICTION_APP.APP_CODE.TRANSFORMED_TABLE_VIEW LIMIT 10;\n\n-- Next we call the procedure that infers our model\nUSE WAREHOUSE TRAINING_WH;\nCALL CREDIT_CARD_PREDICTION_APP.APP_CODE.CC_BATCH_PROCESSING();\n\n-- And we can now see the scored table\nSELECT * FROM CREDIT_CARD_PREDICTION_APP.APP_CODE.SCORED_TABLE_VIEW LIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe are satisfied with the testing. The next step is to distribute the app.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDistribute Native App\u003C/h2\u003E\n","\u003Ch3\u003EProvider Account (Zamboni)\u003C/h3\u003E\n","\u003Cp\u003EWe need to create a version before and a release directive before we can share. We can do this in the manifest file, or with the code below\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003E\nALTER APPLICATION PACKAGE CREDIT_CARD_PREDICTION_PACKAGE\n  ADD VERSION v1\n  USING '@CREDIT_CARD_PREDICTION_PACKAGE.MODEL_ASSETS.MODEL_STAGE'\n  LABEL = 'Credit Card Default Model Version 1.0';\n\nSHOW VERSIONS IN APPLICATION PACKAGE CREDIT_CARD_PREDICTION_PACKAGE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBelow is how we set a Release Directive. A Release Directive is a way for Providers to control which Apps versions are available for their consumers. More information can be found in the \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/native-apps/update-app-release-directive\"\u003Edocumentation\u003C/a\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EALTER APPLICATION PACKAGE CREDIT_CARD_PREDICTION_PACKAGE\n  SET DEFAULT RELEASE DIRECTIVE\n  VERSION = v1\n  PATCH = 0;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow we can distribute the app to our consumer - Snowbank.\u003C/p\u003E\n","\u003Cp\u003EOn the left panel, navigate to Data Products > Provider Studio\u003C/p\u003E\n","\u003Cp\u003ESelect Create Listing > Internal Listing\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-collaboration-native-app/internal_listing_ui.png\" alt=\"Diagram\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFill in the name of the listing in the UI as "Credit Card Default Model". Then select "Add data product"\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-collaboration-native-app/add_data_product_ui.png\" alt=\"Diagram\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESelect the CREDIT_CARD_PREDICTION_PACKAGE, and click save.\u003C/p\u003E\n","\u003Cp\u003ESelect "Access Control" to open up the modal. Under "Who can access this data product?" select "Selected accounts and roles". In the Accounts dropdown, select SNOWBANK.\u003C/p\u003E\n","\u003Cp\u003EFor the Allow Discovery section, select "Not discoverable by users without access".\u003C/p\u003E\n","\u003Cp\u003EClick Save\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-collaboration-native-app/package_listing_ui.png\" alt=\"Diagram\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAdd a support email to the listing.\u003C/p\u003E\n","\u003Cp\u003EFinally, click Publish.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConsume Model via Native App\u003C/h2\u003E\n","\u003Cp\u003EIn this next step, we will log in to our consumer account (Snowbank). You should have saved the details such as the account URL and username from step 3. Log in to the Snowbank Account, and follow the steps to reset your password.\u003C/p\u003E\n","\u003Cp\u003ENext, lets ingest the data into this completely separate account by running the following commands in a SQL worksheet.\u003C/p\u003E\n","\u003Ch3\u003ESetup and Load Data\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE WAREHOUSE QUERY_WH WITH \n  WAREHOUSE_SIZE = 'X-SMALL' \n  WAREHOUSE_TYPE = 'STANDARD' \n  AUTO_SUSPEND = 300 \n  AUTO_RESUME = TRUE \n  MIN_CLUSTER_COUNT = 1 \n  MAX_CLUSTER_COUNT = 1;\n\nCREATE OR REPLACE DATABASE NATIVE_APP_CONSUMER;\nCREATE OR REPLACE SCHEMA NATIVE_APP_CONSUMER;\nUSE SCHEMA NATIVE_APP_CONSUMER.NATIVE_APP_CONSUMER;\n\nCREATE OR REPLACE FILE FORMAT parquet_format\nTYPE = PARQUET;\n\nCREATE OR REPLACE STAGE quickstart_cc_default_unscored_data\n    URL = 's3://sfquickstarts/two_way_data_share/unscored/'\n    FILE_FORMAT = parquet_format;\n\nCREATE OR REPLACE TABLE cc_default_unscored_data \n  USING TEMPLATE (\n    SELECT ARRAY_AGG(OBJECT_CONSTRUCT(*))\n      FROM TABLE(\n        INFER_SCHEMA(\n          LOCATION=>'@quickstart_cc_default_unscored_data',\n          FILE_FORMAT=>'parquet_format'\n        )\n      ));\n\nALTER WAREHOUSE query_wh SET warehouse_size=MEDIUM;\n\nCOPY INTO cc_default_unscored_data \n  FROM @quickstart_cc_default_unscored_data \n  FILE_FORMAT = (FORMAT_NAME= 'parquet_format') \n  MATCH_BY_COLUMN_NAME=CASE_INSENSITIVE;\n\nALTER WAREHOUSE query_wh SET warehouse_size=XSMALL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAccept Shared Model\u003C/h3\u003E\n","\u003Cp\u003EOn the left menu, navigate through to Data Products > Marketplace. Then select the Internal Marketplace tab. You should see your app ready to be installed.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-collaboration-native-app/listing_consumer_side_ui.png\" alt=\"Diagram\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESelect Credit Card Default Model, and on the next screen, select Get. If you do not see the Get button, ensure that you are operating as the ACCOUNTADMIN role. Wait for it to install, and then click "Configure".\u003C/p\u003E\n","\u003Cp\u003ESelect the Privileges tab, and then click "Add" to let our App refer to data in the consumer account\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-collaboration-native-app/consumer_app_config_ui.png\" alt=\"Diagram\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick "Select Data" and navigate to the CC_DEFAULT_UNSCORED_DATA in the NATIVE_APP_CONSUMER database and the NATIVE_APP_CONSUMER schema. Click save.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUse Shared Model\u003C/h2\u003E\n","\u003Ch3\u003EConsumer Account (Snowbank)\u003C/h3\u003E\n","\u003Cp\u003EOpen up a SQL Worksheet, and run the tests again. This time, the model has been shared with a segregated account, and the data that is being used for inference has not left the account.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003E-- This should be empty, as we have not called the procedure to populate the table yet\nSELECT * FROM CREDIT_CARD_DEFAULT_MODEL.APP_CODE.RAW_TABLE_VIEW;\n\n-- Now we call the procedure\nCALL CREDIT_CARD_DEFAULT_MODEL.APP_CODE.CREATE_TABLE_FROM_REFERENCE_RAW();\n\n-- And the table should now be populated\nSELECT * FROM CREDIT_CARD_DEFAULT_MODEL.APP_CODE.RAW_TABLE_VIEW LIMIT 10;\nSELECT COUNT(*) FROM CREDIT_CARD_DEFAULT_MODEL.APP_CODE.RAW_TABLE_VIEW;\n\n-- Next we call the procedure to do the feature engineering. It will take 5 minutes to complete\nCALL CREDIT_CARD_DEFAULT_MODEL.APP_CODE.CC_PROFILE_PROCESSING();\n\n-- The feature table should now be populated\nSELECT * FROM  CREDIT_CARD_DEFAULT_MODEL.APP_CODE.TRANSFORMED_TABLE_VIEW LIMIT 10;\n\n-- Next we call the procedure that infers our model. This will take 5 mins to complete\nCALL CREDIT_CARD_DEFAULT_MODEL.APP_CODE.CC_BATCH_PROCESSING();\n\n-- And we can now see the scored table\nSELECT * FROM CREDIT_CARD_DEFAULT_MODEL.APP_CODE.SCORED_TABLE_VIEW LIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe have successfully used a ML Model shared to us within our own environment.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClean Up\u003C/h2\u003E\n","\u003Ch3\u003EConsumer Account (Snowbank)\u003C/h3\u003E\n","\u003Cp\u003ERun the following SQL commands\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUSE ROLE ACCOUNTADMIN;\nDROP APPLICATION CREDIT_CARD_DEFAULT_MODEL;\nDROP DATABASE NATIVE_APP_CONSUMER;\nDROP WAREHOUSE QUERY_WH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EProvider Account (Zamboni)\u003C/h3\u003E\n","\u003Cp\u003ENavigate to Data Products > Provider Studio > Listings and select Credit Card Default Model\u003C/p\u003E\n","\u003Cp\u003EClick the vertical ellipses in the top right, and the select Unpublish\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-collaboration-native-app/clean_up_unpublish_ui.png\" alt=\"Diagram\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick the vertical ellipses again and select "Delete"\u003C/p\u003E\n","\u003Cp\u003EOpen a new SQL worksheet and run the following\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUSE ROLE ACCOUNTADMIN;\nDROP APPLICATION IF EXISTS CREDIT_CARD_PREDICTION_APP;\nDROP APPLICATION PACKAGE CREDIT_CARD_PREDICTION_PACKAGE;\nDROP DATABASE IF EXISTS NATIVE_APP_DEMO;\nDROP WAREHOUSE TRAINING_WH;\nDROP WAREHOUSE QUERY_WH;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations, you have just shared an ML Model over the Snowflake Marketplace via the Snowflake Native App framework. By sharing the ML Model to the Consumer account, we have realised the following benefits:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EData Sovereignty and Security for the Consumer. With Native Apps, the app logic is brought to the consumers dataset, so no customer data is leaving their security perimeter\u003C/li\u003E\u003Cli\u003ECompliance Risk Reduction for the Provider. Similar to the above, the provider of the model may not want to have access to customer data. This requires extra consideration with how to securely handle this data, which is not necessary if the model is shared to their customers to run against their own data.\u003C/li\u003E\u003Cli\u003EIncreased Margin. By sharing the model to the consumer, the infrastructure cost to execute the model are now with the consumer. This means the provider can monetise the model, without worrying about infrastructure costs. Similarly the consumer can set warehouse sizes appropriate to the SLAs and budgets of the consumer organisation.\u003C/li\u003E\u003Cli\u003EFaster Onboarding. By having a replicable model, the provider can simple leverage the Snowflake Marketplace for distribution. They no longer need to go through a lengthy process of approvals to set up a data pipeline with their customers.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to train an ML model in Snowflake\u003C/li\u003E\u003Cli\u003EHow to package the ML Model in an Application Package\u003C/li\u003E\u003Cli\u003EHow to privately list a Native Application\u003C/li\u003E\u003Cli\u003EHow to consume a Native Application\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-model-sharing-using-native-app\"\u003EGitHub Repo for Quickstart Assets\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/native-apps/native-apps-about\"\u003ESnowflake Developer Documentation for Native Applications\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/native-apps/overview\"\u003ECLI for Native Apps\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-ml/index\"\u003EDeveloper Guide on End-to-End Machine Learning in Snowflake\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Turn ML models into Snowflake Native Apps for secure data collaboration and sharing with customers, partners, and third-party data consumers.","title":"Getting Started With Model Sharing Using Native Apps",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\nThis Quickstart guide is the second in a series of Collaboration & Applications in Snowflake. Part One of the series can be found [here](/en/developers/guides/data-collaboration-two-way/)\n\nIn Part One, we showed how using Snowflake's unique collaboration features, we could:\n- share data from an organisation to another\n- score that data using an ML model\n- share it back with the original provider\nall in an automated pipeline\n\nIn this Quickstart, we will show how we can leverage the Native Apps Framework to simplify this process even further.\n\nThe Native App Framework is a collaboration framework that allows providers to share data and/or related business logic to other Snowflake customers. In the previous Quickstart guide, we set up a bi-directional share between two organisations. With the Native App framework, we can simplify this relationship by allowing one party to share its logic in the form of an application to the other. Therefore, we have brought the app to the data, as opposed to bringing the data to the app\n\n![Diagram](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-collaboration-native-app/native_app_overview.png)\n\nThere are numerous benefits to leveraging the Native App framework for our Use Case. For example:\n- Data Sovereignty and Security for the Consumer. In the previous quickstart, it was necessary for the provider to share data with another entity. This could be potentially sensitive information, and therefore a bigger hurdle for security and governance teams to approve. With Native Apps, the app logic is brought to the consumers dataset, so no customer data is leaving their security perimeter\n- Compliance Risk Reduction for the Provider. Similar to the above, the provider of the model may not want to have access to customer data. This requires extra consideration with how to securely handle this data, which is not necessary if the model is shared to their customers to run against their own data.\n- Increased Margin. By sharing the model to the consumer, the infrastructure cost to execute the model are now with the consumer. This means the provider can monetise the model, without worrying about infrastructure costs. Similarly the consumer can set warehouse sizes appropriate to the SLAs and budgets of the consumer organisation.\n- Faster Onboarding. By having a replicable model, the provider can simply leverage the Snowflake Marketplace for distribution. They no longer need to go through a lengthy process of approvals to set up a data pipeline with their customers.\n\n### What You Will Learn \n- How to train an ML model in Snowflake\n- How to package the ML Model in an Application Package\n- How to privately list a Native Application \n- How to Consume a Native Application \n\n### What You Will Build \n- A Native Application that contains an ML Model shared via a Private Listing\n\nThe architecture of what we will build is shown below\n\n![Diagram](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-collaboration-native-app/model_na_architecture.png)\n\n### Prerequisites\n- [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) installed\n    **Download the git repo here: https://github.com/Snowflake-Labs/sfguide-data-collaboration-native-app**\n- [Anaconda](https://www.anaconda.com/) installed\n- [Python 3.10](https://www.python.org/downloads/) installed\n    - Note that you will be creating a Python environment with 3.10 in the **Consumer Account - Create Model** step\n- Snowflake accounts 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). If you do not have a Snowflake account, you can register for a [free trial account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides).\n- Snowflake accounts with the Snowflake Marketplace T&C's accepted. This will allow you to create Listings.\n- A Snowflake account login with a role that has the ability to create Listings, Databases, Schemas, Tables, Stages, User-Defined Functions, and Stored Procedures. If not, you will need to register for free trials or use a different role.\n- The [Snowpark ML](https://docs.snowflake.com/developer-guide/snowpark-ml/index#installing-snowpark-ml-from-the-snowflake-conda-channel) package installed\n\n\u003C!-- ------------------------ --\u003E\n## Business Use Case and Context\n\nThe Business Use case follows a similar scenario as Part One.\n\nIn this guide, we are playing the role of Zamboni and Snowbank. The Credit Risk team at Snowbank has noticed a rise in credit card default rates which affects the bottom line of the business. Previously, it has shared data with an external organisation (Zamboni) who assist with analysing the data and scoring which customers are most likely to default.\n\nThis time, the compliance team has said that it is too risky to share this customer data with an external party without proper procedures being followed, which could take a few months to complete. Zamboni have proposed a solution that utilises the Native Application Framework.\n\nSince both companies use Snowflake, Zamboni has proposed sharing their proprietary Credit Default Scoring Model via the Native Application Framework. The advantages of doing this for Snowbank are:\n- No customer data leaves Snowbank's Snowflake Account\n- The Native App runs within the Snowbank Account. Since Snowflake has been approved for use internally, it is not subject to longer onboarding and cybersecurity checks\n\nThe advantages for Zamboni are:\n- Simple deployment. Zamboni can simply share the model using a similar listing paradigm as sharing a dataset.\n- Reduced compliance risk. Zamboni's security and compliance team are similarly pleased in being able to satisfy the business needs without needing Snowbank's data hosted on their servers\n- Increased Margin. Zamboni are now able to share their proprietary logic safely, without having to provision infrastructure to run the model on behalf of Snowbank.\n- Speed to Market. Zamboni can simply distribute the model via the Native App framework in a private listing. They no longer need to set up data pipelines. They can also build once, and distribute to lots of different customers by leveraging Snowflake's multitenancy framework.\n\nWhat is different in this scenario compared to Part One, is that it is no longer a bi-directional share of data. Now Zamboni becomes the provider of an Application, and Snowbank becomes the consumer.\n\n### Dataset Details\n\nWe will use a publicly available dataset to train our ML model for the purposes of this demo. The dataset contains aggregated profile features for each customer at each statement date. Features are anonymized and normalized, and fall into the following general categories:\n\nD_* = Delinquency variables\nS_* = Spend variables\nP_* = Payment variables\nB_* = Balance variables\nR_* = Risk variables\n\n### Dataset Citation\n\nAddison Howard, AritraAmex, Di Xu, Hossein Vashani, inversion, Negin, Sohier Dane. (2022). American Express - Default Prediction. Kaggle. https://kaggle.com/competitions/amex-default-prediction\n\n\u003C!-- ------------------------ --\u003E\n## Setup\n\nNavigate to the [Snowflake Trial Landing Page](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides). Follow the prompts to create a Snowflake Account. You should receive an email to activate your trial account.\n\nNavigate to a new worksheet and execute the following commands to create a second account in the Organisation. The second account will be the account we share the model to (Snowbank). In trial accounts we cannot share the Native App externally, although the same process can be followed to share the app externally if you have a non-trial account.\n\n```SQL\nUSE ROLE ORGADMIN;\n\nCREATE ACCOUNT SNOWBANK\n  ADMIN_NAME = ADMIN\n  ADMIN_PASSWORD = 'ENTER PASSWORD HERE'\n  EMAIL = 'ENTER YOUR EMAIL HERE'\n  EDITION = ENTERPRISE;\n```\nNote down the account locator and the url from the output of the command above for use in later steps\n\n\n### Provider Account (Zamboni)\n\nIn this part of the lab we'll set up our Provider Snowflake account. In our business scenario, this step represents Zamboni developing their proprietary Credit Card Default model from their own datasets. Stay in the same account from the previous section.\n\n### Initial Set Up\n\nNext, open up a new worksheet and run all following steps as the ACCOUNTADMIN role\n\n```SQL\n  -- Change role to accountadmin\n  USE ROLE ACCOUNTADMIN;\n```\n\nNext we create two [Virtual Warehouses](https://docs.snowflake.com/en/user-guide/warehouses-overview) that can be used to load the initial dataset, and do our model training respectively.\n\n```SQL\n-- Create a virtual warehouse for loading and ad-hoc queries\nCREATE OR REPLACE WAREHOUSE QUERY_WH WITH \n  WAREHOUSE_SIZE = 'X-SMALL' \n  WAREHOUSE_TYPE = 'STANDARD' \n  AUTO_SUSPEND = 300 \n  AUTO_RESUME = TRUE \n  MIN_CLUSTER_COUNT = 1 \n  MAX_CLUSTER_COUNT = 1;\n\n-- Create a virtual warehouse for training\nCREATE OR REPLACE WAREHOUSE training_wh with \n  WAREHOUSE_SIZE = 'MEDIUM' \n  WAREHOUSE_TYPE = 'snowpark-optimized' \n  MAX_CLUSTER_COUNT = 1\n  AUTO_SUSPEND = 300 \n  AUTO_RESUME = TRUE;\n```\n\n### Load Data \n\nNext we will create a database and schema that will house the tables to train our model.\n\n```SQL\n-- Create the application database and schema\nCREATE OR REPLACE DATABASE NATIVE_APP_DEMO;\nCREATE OR REPLACE SCHEMA NATIVE_APP_DEMO;\nUSE SCHEMA NATIVE_APP_DEMO.NATIVE_APP_DEMO;\n```\n\nOur data is in Parquet format, so we will create a file format object\n\n```SQL\nCREATE OR REPLACE FILE FORMAT parquet_format\nTYPE = PARQUET;\n```\n\nNext we set up our [External Stage](https://docs.snowflake.com/en/user-guide/data-load-s3-create-stage#external-stages) to our data. In our business scenario, we would have a secure [Storage Integration](https://docs.snowflake.com/en/sql-reference/sql/create-storage-integration) to the external stage rather than a public s3 bucket.\n\n```SQL\nCREATE OR REPLACE STAGE quickstart_cc_default_training_data\n    URL = 's3://sfquickstarts/two_way_data_share/train/'\n    FILE_FORMAT = parquet_format;\n```\n\nThis DDL will create the structure for the table which is the main source of data for our lab.\n\n```SQL\nCREATE OR REPLACE TABLE cc_default_training_data\n  USING TEMPLATE (\n    SELECT ARRAY_AGG(OBJECT_CONSTRUCT(*))\n      FROM TABLE(\n        INFER_SCHEMA(\n          LOCATION=\u003E'@quickstart_cc_default_training_data',\n          FILE_FORMAT=\u003E'parquet_format'\n        )\n      ));\n```\n\nNow we will load the data in the tables. We can scale up the warehouse temporarily so we do not have to wait as long\n\n```SQL\nALTER WAREHOUSE query_wh SET warehouse_size=MEDIUM;\n```\n\nThe code below loads the data in to the tables.\n\n```SQL\nCOPY INTO cc_default_training_data \n  FROM @quickstart_cc_default_training_data \n  FILE_FORMAT = (FORMAT_NAME= 'parquet_format') \n  MATCH_BY_COLUMN_NAME=CASE_INSENSITIVE;\n```\nWe can now scale down the warehouse since we have finished loading data for the lab\n\n```SQL\nALTER WAREHOUSE query_wh SET warehouse_size=XSMALL;\n```\n\nYou should have loaded over 5.5 million rows in a few minutes. To check, query the data in the worksheet\n\n```SQL\nSELECT COUNT(*) \n  FROM CC_DEFAULT_TRAINING_DATA;\n```\n\nWe have loaded all the data in Zamboni. We can now proceed with training the model\n\n\u003C!-- ------------------------ --\u003E\n## Create Model\n\n### Provider Account (Zamboni)\n\nFor this section, make sure you download the corresponding [git repo](https://github.com/Snowflake-Labs/sfguide-getting-started-with-model-sharing-using-native-app) so you have the files referenced in this section.\n\n### Set Up Snowpark for Python and Snowpark ML\n\nThe first step is to set up the python environment to develop our model. To do this:\n\n- 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- Open a new terminal window in the downloaded directory with the conda_env.yml file and execute the following commands in the same terminal window:\n\n  1. Create the conda environment.\n  ```\n  conda env create -f conda_env.yml\n  ```\n\n  2. Activate the conda environment.\n  ```\n  conda activate data-collaboration-native-app\n  ```\n\n  3. Start notebook server:\n  ```\n  jupyter notebook\n  ```\n  Open the jupyter notebook Credit Card Default Native App Notebook\n\n- Update connection.json with your Snowflake Account Identifier details and User Name in a code editor of your choice.\n  Here's a sample based on the object names we created in the last step:\n\n```\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\"           : \"ACCOUNTADMIN\",\n  \"warehouse\"      : \"QUERY_WH\",\n  \"database\"       : \"NATIVE_APP_DEMO\",\n  \"schema\"         : \"NATIVE_APP_DEMO\"\n}\n```\n\nYou can find your account identifier by running the following SQL\n\n```SQL\nSELECT CURRENT_ORGANIZATION_NAME() || '-' || CURRENT_ACCOUNT_NAME();\n```\n\n### Train and Register Model\nOpen up the Credit Card Default Native App notebook and follow the steps. Once you have completed those, you will have trained and deployed a ML Model in Snowflake that predicts credit card default risk. You will also have registered the model in the Snowflake Model Registry.\n\nStay in the Zamboni account for the next step.\n\n\u003C!-- ------------------------ --\u003E\n## Build Native App\n\n### Provider Account (Zamboni)\n\nNow we have trained the model, we want to encapsulate it in an Application Package, so we can distribute it via the Native App Framework. More information on the Native Application framework can be found in the documentation [here](https://docs.snowflake.com/en/developer-guide/native-apps/native-apps-about). We will be working through the [Native App Development Workflow](https://docs.snowflake.com/en/developer-guide/native-apps/native-apps-workflow#development-workflow).\n\nContinue to work through the python notebook. The SQL commands below can be run from the python notebook.\n\nThe next step is to create an Application Package object that will hold the assets we wish to distribute to Snowbank. An Application Package encapsulates the data content, application logic, metadata, and setup script required by an application. An Application Package also contains information about versions and patch levels defined for the application. We do this by running the following SQL.\n\n```SQL\nGRANT CREATE APPLICATION PACKAGE ON ACCOUNT TO ROLE ACCOUNTADMIN;\nCREATE APPLICATION PACKAGE IF NOT EXISTS CREDIT_CARD_PREDICTION_APP_PACKAGE;\nSHOW APPLICATION PACKAGES;\n```\n\nNext, we need to create a stage within the Application Package for us to put our model assets. We run the following SQL commands\n\n```SQL\nUSE APPLICATION PACKAGE CREDIT_CARD_PREDICTION_PACKAGE;\nCREATE SCHEMA IF NOT EXISTS MODEL_ASSETS;\nCREATE OR REPLACE STAGE CREDIT_CARD_PREDICTION_APP_PACKAGE.MODEL_ASSETS.MODEL_STAGE FILE_FORMAT = (TYPE = 'csv' FIELD_DELIMITER = '|' SKIP_HEADER = 1);\n```\n\nSo we share a model that is consistent with what is in the model registry, we are going to export the model from the registry, and put it in the Application Package. We do this in the following code block\n\n```python\n# Retrieve the model from the registry\nm = registry.get_model(\"NATIVE_APP_DEMO.NATIVE_APP_DEMO.CREDIT_CARD_DEFAULT_MODEL\")\nmv = m.version(\"default\")\n\n# Create a temp directory to export the model\nimport tempfile, pathlib, shutil, os\nlocal_dir = pathlib.Path(tempfile.mkdtemp(prefix=\"model_export_\"))\n\n# export the full artefact set into that folder\nmv.export(local_dir.as_posix(), export_mode=ExportMode.FULL)\n\n# Upload the files and folder structure to the Application Package Stage\nstage_root = \"@CREDIT_CARD_PREDICTION_PACKAGE.MODEL_ASSETS.MODEL_STAGE/models\"\n\nfor file_path in pathlib.Path(local_dir).rglob('*'):\n    if file_path.is_file():\n        rel_path = file_path.relative_to(local_dir).as_posix()   # e.g. runtimes/env.yml or manifest.yml\n        subdir   = os.path.dirname(rel_path)                     # '' or 'runtimes'\n\n        # remote prefix **must end with a slash** and contain no file-name\n        remote_prefix = f\"{stage_root}/{subdir}/\" if subdir else f\"{stage_root}/\"\n\n        session.file.put(\n            file_path.as_posix(),\n            remote_prefix,\n            overwrite=True,\n            auto_compress=False\n        )\n\n# Check that all our artefacts have been uploaded\nsession.sql(\"LIST @CREDIT_CARD_PREDICTION_PACKAGE.MODEL_ASSETS.MODEL_STAGE/models;\").collect()\n```\n\nAn application requires a manifest file, and a setup script (as outlined in the Native App Development Workflow). We upload them to our Application Package in the following commands\n\n```SQL\nPUT file://scripts/setup.sql @CREDIT_CARD_PREDICTION_APP_PACKAGE.MODEL_ASSETS.MODEL_STAGE/scripts overwrite=true auto_compress=false;\n\nPUT file://manifest.yml @CREDIT_CARD_PREDICTION_APP_PACKAGE.MODEL_ASSETS.MODEL_STAGE overwrite=true auto_compress=false;\n```\n\nThe setup script contains the SQL statements that define the components created when a consumer installs your application. For us, this will include creating stored procedures to perform feature engineering, as well as registering our model for the app to use on the consumer side (documentation [here](https://docs.snowflake.com/LIMITEDACCESS/native-apps/models)). This is done in the following lines that were run as part of setup.sql on installation of the app:\n\n```SQL\ncreate or replace model APP_CODE.CREDIT_CARD_DEFAULT_MODEL from @CREDIT_CARD_PREDICTION_PACKAGE.MODEL_ASSETS.MODEL_STAGE/models/;\n\ngrant usage on model APP_CODE.CREDIT_CARD_DEFAULT_MODEL to application role app_user;\n```\n\n\n\n\u003C!-- ------------------------ --\u003E\n## Perform Local Testing\n\n### Provider Account (Zamboni)\n\nNow the Application has been created from the Application Package in our Provider Account, we can perform some testing to ensure it behaves as expected. In this Quickstart, we have exposed more views and functions than what is necessary, so we can follow along and see how the app in functioning.\n\nYou will need a SQL worksheet for this part of the Quickstart. We can load some testing data by executing the following:\n\n```SQL\nUSE SCHEMA NATIVE_APP_DEMO.NATIVE_APP_DEMO;\n\nCREATE OR REPLACE STAGE quickstart_cc_default_unscored_data\n    URL = 's3://sfquickstarts/two_way_data_share/unscored/'\n    FILE_FORMAT = parquet_format;\n\n-- Create below to be the template of the above (without the target column)\nCREATE OR REPLACE TABLE cc_default_unscored_data LIKE cc_default_training_data;\nALTER TABLE cc_default_unscored_data DROP COLUMN \"target\";\n\nALTER WAREHOUSE query_wh SET warehouse_size=MEDIUM;\n\nUSE WAREHOUSE query_wh;\n\nCOPY INTO cc_default_unscored_data \n  FROM @quickstart_cc_default_unscored_data \n  FILE_FORMAT = (FORMAT_NAME= 'parquet_format') \n  MATCH_BY_COLUMN_NAME=CASE_INSENSITIVE;\n\nALTER WAREHOUSE query_wh SET warehouse_size=XSMALL;\n```\n\nThis app requires the consumer to explicitly provide a reference to a table in the Snowflake instance it is installed as an input. References are a way for Native App consumers to be explicit in their permissioning to any existing object in a Snowflake account that is required. This ensures that Native Applications run as \"isolated objects\" within a Consumer Account, and must be granted explicit permission to access other objects. More information can be foind [here](https://docs.snowflake.com/en/developer-guide/native-apps/requesting-refs). We can achieve referencing through the UI, or running the following SQL.\n\n```SQL\n-- Bind the reference to a table in our own Snowflake Account. This can be done via SQL or the UI\nCALL credit_card_prediction_app.app_code.register_single_reference('RAW_TABLE' , 'ADD', SYSTEM$REFERENCE('TABLE', 'NATIVE_APP_DEMO.NATIVE_APP_DEMO.CC_DEFAULT_UNSCORED_DATA', 'PERSISTENT', 'SELECT'));\n\n-- Check that the reference worked\nSHOW REFERENCES IN APPLICATION credit_card_prediction_app;\n```\n\nNext let's run through the commands and see if they are working as expected. Note that we are running code inside an isolated Application object. This application object will eventually be shared with our consumer (Snowbank).\n\n```SQL\n-- This should be empty, as we have not called the procedure to populate the table yet\nSELECT * FROM CREDIT_CARD_PREDICTION_APP.APP_CODE.RAW_TABLE_VIEW;\n\n-- Now we call the procedure\nCALL CREDIT_CARD_PREDICTION_APP.APP_CODE.CREATE_TABLE_FROM_REFERENCE_RAW();\n\n-- And the table should now be populated\nSELECT * FROM CREDIT_CARD_PREDICTION_APP.APP_CODE.RAW_TABLE_VIEW LIMIT 10;\nSELECT COUNT(*) FROM CREDIT_CARD_PREDICTION_APP.APP_CODE.RAW_TABLE_VIEW;\n\n-- Next we call the procedure to do the feature engineering. This will take 5 mins to complete\nCALL CREDIT_CARD_PREDICTION_APP.APP_CODE.CC_PROFILE_PROCESSING();\n\n-- The feature table should now be populated\nSELECT * FROM CREDIT_CARD_PREDICTION_APP.APP_CODE.TRANSFORMED_TABLE_VIEW LIMIT 10;\n\n-- Next we call the procedure that infers our model\nUSE WAREHOUSE TRAINING_WH;\nCALL CREDIT_CARD_PREDICTION_APP.APP_CODE.CC_BATCH_PROCESSING();\n\n-- And we can now see the scored table\nSELECT * FROM CREDIT_CARD_PREDICTION_APP.APP_CODE.SCORED_TABLE_VIEW LIMIT 10;\n```\nWe are satisfied with the testing. The next step is to distribute the app.\n\n\u003C!-- ------------------------ --\u003E\n## Distribute Native App\n\n### Provider Account (Zamboni)\n\nWe need to create a version before and a release directive before we can share. We can do this in the manifest file, or with the code below\n\n```SQL\n\nALTER APPLICATION PACKAGE CREDIT_CARD_PREDICTION_PACKAGE\n  ADD VERSION v1\n  USING '@CREDIT_CARD_PREDICTION_PACKAGE.MODEL_ASSETS.MODEL_STAGE'\n  LABEL = 'Credit Card Default Model Version 1.0';\n\nSHOW VERSIONS IN APPLICATION PACKAGE CREDIT_CARD_PREDICTION_PACKAGE;\n```\n\nBelow is how we set a Release Directive. A Release Directive is a way for Providers to control which Apps versions are available for their consumers. More information can be found in the [documentation](https://docs.snowflake.com/en/developer-guide/native-apps/update-app-release-directive)\n\n```SQL\nALTER APPLICATION PACKAGE CREDIT_CARD_PREDICTION_PACKAGE\n  SET DEFAULT RELEASE DIRECTIVE\n  VERSION = v1\n  PATCH = 0;\n```\n\nNow we can distribute the app to our consumer - Snowbank.\n\nOn the left panel, navigate to Data Products \u003E Provider Studio\n\nSelect Create Listing \u003E Internal Listing\n\n![Diagram](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-collaboration-native-app/internal_listing_ui.png)\n\nFill in the name of the listing in the UI as \"Credit Card Default Model\". Then select \"Add data product\"\n\n![Diagram](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-collaboration-native-app/add_data_product_ui.png)\n\nSelect the CREDIT_CARD_PREDICTION_PACKAGE, and click save.\n\nSelect \"Access Control\" to open up the modal. Under \"Who can access this data product?\" select \"Selected accounts and roles\". In the Accounts dropdown, select SNOWBANK.\n\nFor the Allow Discovery section, select \"Not discoverable by users without access\".\n\nClick Save\n\n![Diagram](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-collaboration-native-app/package_listing_ui.png)\n\nAdd a support email to the listing.\n\nFinally, click Publish.\n\n\n\u003C!-- ------------------------ --\u003E\n## Consume Model via Native App\n\nIn this next step, we will log in to our consumer account (Snowbank). You should have saved the details such as the account URL and username from step 3. Log in to the Snowbank Account, and follow the steps to reset your password.\n\nNext, lets ingest the data into this completely separate account by running the following commands in a SQL worksheet.\n\n### Setup and Load Data\n\n```SQL\nUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE WAREHOUSE QUERY_WH WITH \n  WAREHOUSE_SIZE = 'X-SMALL' \n  WAREHOUSE_TYPE = 'STANDARD' \n  AUTO_SUSPEND = 300 \n  AUTO_RESUME = TRUE \n  MIN_CLUSTER_COUNT = 1 \n  MAX_CLUSTER_COUNT = 1;\n\nCREATE OR REPLACE DATABASE NATIVE_APP_CONSUMER;\nCREATE OR REPLACE SCHEMA NATIVE_APP_CONSUMER;\nUSE SCHEMA NATIVE_APP_CONSUMER.NATIVE_APP_CONSUMER;\n\nCREATE OR REPLACE FILE FORMAT parquet_format\nTYPE = PARQUET;\n\nCREATE OR REPLACE STAGE quickstart_cc_default_unscored_data\n    URL = 's3://sfquickstarts/two_way_data_share/unscored/'\n    FILE_FORMAT = parquet_format;\n\nCREATE OR REPLACE TABLE cc_default_unscored_data \n  USING TEMPLATE (\n    SELECT ARRAY_AGG(OBJECT_CONSTRUCT(*))\n      FROM TABLE(\n        INFER_SCHEMA(\n          LOCATION=\u003E'@quickstart_cc_default_unscored_data',\n          FILE_FORMAT=\u003E'parquet_format'\n        )\n      ));\n\nALTER WAREHOUSE query_wh SET warehouse_size=MEDIUM;\n\nCOPY INTO cc_default_unscored_data \n  FROM @quickstart_cc_default_unscored_data \n  FILE_FORMAT = (FORMAT_NAME= 'parquet_format') \n  MATCH_BY_COLUMN_NAME=CASE_INSENSITIVE;\n\nALTER WAREHOUSE query_wh SET warehouse_size=XSMALL;\n```\n\n### Accept Shared Model\n\nOn the left menu, navigate through to Data Products \u003E Marketplace. Then select the Internal Marketplace tab. You should see your app ready to be installed.\n\n![Diagram](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-collaboration-native-app/listing_consumer_side_ui.png)\n\nSelect Credit Card Default Model, and on the next screen, select Get. If you do not see the Get button, ensure that you are operating as the ACCOUNTADMIN role. Wait for it to install, and then click \"Configure\".\n\nSelect the Privileges tab, and then click \"Add\" to let our App refer to data in the consumer account\n\n![Diagram](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-collaboration-native-app/consumer_app_config_ui.png)\n\nClick \"Select Data\" and navigate to the CC_DEFAULT_UNSCORED_DATA in the NATIVE_APP_CONSUMER database and the NATIVE_APP_CONSUMER schema. Click save.\n\n\u003C!-- ------------------------ --\u003E\n## Use Shared Model\n\n### Consumer Account (Snowbank)\n\nOpen up a SQL Worksheet, and run the tests again. This time, the model has been shared with a segregated account, and the data that is being used for inference has not left the account.\n\n```SQL\n-- This should be empty, as we have not called the procedure to populate the table yet\nSELECT * FROM CREDIT_CARD_DEFAULT_MODEL.APP_CODE.RAW_TABLE_VIEW;\n\n-- Now we call the procedure\nCALL CREDIT_CARD_DEFAULT_MODEL.APP_CODE.CREATE_TABLE_FROM_REFERENCE_RAW();\n\n-- And the table should now be populated\nSELECT * FROM CREDIT_CARD_DEFAULT_MODEL.APP_CODE.RAW_TABLE_VIEW LIMIT 10;\nSELECT COUNT(*) FROM CREDIT_CARD_DEFAULT_MODEL.APP_CODE.RAW_TABLE_VIEW;\n\n-- Next we call the procedure to do the feature engineering. It will take 5 minutes to complete\nCALL CREDIT_CARD_DEFAULT_MODEL.APP_CODE.CC_PROFILE_PROCESSING();\n\n-- The feature table should now be populated\nSELECT * FROM  CREDIT_CARD_DEFAULT_MODEL.APP_CODE.TRANSFORMED_TABLE_VIEW LIMIT 10;\n\n-- Next we call the procedure that infers our model. This will take 5 mins to complete\nCALL CREDIT_CARD_DEFAULT_MODEL.APP_CODE.CC_BATCH_PROCESSING();\n\n-- And we can now see the scored table\nSELECT * FROM CREDIT_CARD_DEFAULT_MODEL.APP_CODE.SCORED_TABLE_VIEW LIMIT 10;\n```\n\nWe have successfully used a ML Model shared to us within our own environment.\n\n\u003C!-- ------------------------ --\u003E\n## Clean Up\n\n### Consumer Account (Snowbank)\n\nRun the following SQL commands\n\n```SQL\nUSE ROLE ACCOUNTADMIN;\nDROP APPLICATION CREDIT_CARD_DEFAULT_MODEL;\nDROP DATABASE NATIVE_APP_CONSUMER;\nDROP WAREHOUSE QUERY_WH;\n```\n\n### Provider Account (Zamboni)\n\nNavigate to Data Products \u003E Provider Studio \u003E Listings and select Credit Card Default Model\n\nClick the vertical ellipses in the top right, and the select Unpublish\n\n![Diagram](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-collaboration-native-app/clean_up_unpublish_ui.png)\n\nClick the vertical ellipses again and select \"Delete\"\n\nOpen a new SQL worksheet and run the following\n\n```SQL\nUSE ROLE ACCOUNTADMIN;\nDROP APPLICATION IF EXISTS CREDIT_CARD_PREDICTION_APP;\nDROP APPLICATION PACKAGE CREDIT_CARD_PREDICTION_PACKAGE;\nDROP DATABASE IF EXISTS NATIVE_APP_DEMO;\nDROP WAREHOUSE TRAINING_WH;\nDROP WAREHOUSE QUERY_WH;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nCongratulations, you have just shared an ML Model over the Snowflake Marketplace via the Snowflake Native App framework. By sharing the ML Model to the Consumer account, we have realised the following benefits:\n\n- Data Sovereignty and Security for the Consumer. With Native Apps, the app logic is brought to the consumers dataset, so no customer data is leaving their security perimeter\n- Compliance Risk Reduction for the Provider. Similar to the above, the provider of the model may not want to have access to customer data. This requires extra consideration with how to securely handle this data, which is not necessary if the model is shared to their customers to run against their own data.\n- Increased Margin. By sharing the model to the consumer, the infrastructure cost to execute the model are now with the consumer. This means the provider can monetise the model, without worrying about infrastructure costs. Similarly the consumer can set warehouse sizes appropriate to the SLAs and budgets of the consumer organisation.\n- Faster Onboarding. By having a replicable model, the provider can simple leverage the Snowflake Marketplace for distribution. They no longer need to go through a lengthy process of approvals to set up a data pipeline with their customers.\n\n### What You Learned\n- How to train an ML model in Snowflake\n- How to package the ML Model in an Application Package\n- How to privately list a Native Application \n- How to consume a Native Application \n\n### Resources\n- [GitHub Repo for Quickstart Assets](https://github.com/Snowflake-Labs/sfguide-getting-started-with-model-sharing-using-native-app)\n- [Snowflake Developer Documentation for Native Applications](https://docs.snowflake.com/en/developer-guide/native-apps/native-apps-about)\n- [CLI for Native Apps](https://docs.snowflake.com/en/developer-guide/snowflake-cli/native-apps/overview)\n- [Developer Guide on End-to-End Machine Learning in Snowflake](https://docs.snowflake.com/en/developer-guide/snowpark-ml/index)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-d1cee06e7b","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-078dbd2189",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-6ccf9c744f","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-5aeaff6eed","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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