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This hands-on lab will guide you through building a custom machine learning model to predict customer purchase likelihood by analyzing the complex relationship between website experience metrics and product reviews. Using Snowflake's advanced gen AI and machine learning capabilities, you'll work with a carefully constructed synthetic dataset that simulates real-world scenarios, allowing for controlled exploration of various factors affecting purchase decisions.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA complete ML pipeline that processes and classifies text data, performs sentiment analysis, and builds a predictive model\u003C/li\u003E\u003Cli\u003EA horizontally scalable system to extract insights from customer reviews using both custom processing with OSS LLMs directly and Snowflake Cortex AI\u003C/li\u003E\u003Cli\u003EAn automated workflow to deploy continuous data processing on a scheduled basis\u003C/li\u003E\u003Cli\u003EA dashboard to visualize the impact of user experience features on purchase decisions\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to pipeline unstructured data into your Snowflake workflows\u003C/li\u003E\u003Cli\u003EHow to use Ray for scalable distributed data processing\u003C/li\u003E\u003Cli\u003EHow to process synthetic customer feedback data utilizing open source LLMs for classification and Cortex AI for sentiment analysis\u003C/li\u003E\u003Cli\u003EHow to use custom ML models to assess review quality and helpfulness and train across multiple nodes\u003C/li\u003E\u003Cli\u003EHow to deploy the end to end pipeline to production using ML Jobs and Snowflake Tasks\u003C/li\u003E\u003Cli\u003EHow to evaluate the relative importance of user experience features on purchase decisions\u003C/li\u003E\u003Cli\u003EHow to create dynamic visualizations to communicate insights to stakeholders\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake Account. \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESign up for a 30-day free trial account\u003C/a\u003E, make sure to select AWS WEST\u003C/li\u003E\u003Cli\u003EFamiliarity with Python and ML concepts\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ESetup Environment\u003C/h2\u003E\n","\u003Cp\u003EBefore diving into Ray and distributed processing, let's set up our environment with the necessary database objects and sample data.\u003C/p\u003E\n","\u003Ch3\u003EDownload Setup Files\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EDownload these three files to your local machine:\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-build-ml-models-for-customer-conversions/blob/main/setup.sql\"\u003Esetup.sql\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-build-ml-models-for-customer-conversions/blob/main/notebook.ipynb\"\u003Enotebook.ipynb\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-build-ml-models-for-customer-conversions/blob/main/streamlit.py\"\u003Estreamlit.py\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ERun the Setup Script\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ELog in to your Snowflake account\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to Worksheets:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on the \u003Cstrong\u003EWorksheets\u003C/strong\u003E tab in the left navigation pane\u003C/li\u003E\u003Cli\u003EClick on the \u003Cstrong\u003E+\u003C/strong\u003E button to create a new worksheet\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EExecute the setup script:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOpen the worksheet editor\u003C/li\u003E\u003Cli\u003ECopy and paste the contents from your downloaded \u003Ccode\u003Esetup.sql\u003C/code\u003E file\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003ERun All\u003C/strong\u003E button to execute the script\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe setup script creates the necessary database, schema, tables, and loads sample data for our analysis.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Ccode\u003ECREATE COMPUTE POOL\u003C/code\u003E step is expected to fail on trial accounts\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EImport the Notebook\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to Snowflake Notebooks:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on \u003Cstrong\u003EProjects\u003C/strong\u003E » \u003Cstrong\u003ENotebooks\u003C/strong\u003E in the left navigation menu\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EImport the notebook file:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on the \u003Cstrong\u003E...\u003C/strong\u003E menu in the top right\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EImport .ipynb file\u003C/strong\u003E from the dropdown\u003C/li\u003E\u003Cli\u003ESelect the \u003Ccode\u003Enotebook.ipynb\u003C/code\u003E file you downloaded earlier\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EConfigure the notebook:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFor \u003Cstrong\u003EDatabase\u003C/strong\u003E, select \u003Ccode\u003EHOL_DB\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EFor \u003Cstrong\u003ESchema\u003C/strong\u003E, select \u003Ccode\u003EHOL_SCHEMA\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EFor \u003Cstrong\u003ERuntime\u003C/strong\u003E, select \u003Ccode\u003ERun on Container\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EFor \u003Cstrong\u003ERuntime version\u003C/strong\u003E, Leave as is\u003C/li\u003E\u003Cli\u003EFor \u003Cstrong\u003ECompute pool\u003C/strong\u003E, Leave as is\u003C/li\u003E\u003Cli\u003EFor \u003Cstrong\u003EQuery warehouse\u003C/strong\u003E, select \u003Ccode\u003EHOL_WAREHOUSE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EEnable External Access Integrations\u003C/h3\u003E\n","\u003Cp\u003EAfter creating the notebook, you'll need to enable external access for internet connectivity:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn your newly created notebook, click on the three dots (\u003Cstrong\u003E...\u003C/strong\u003E) in the top right corner\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003ENotebook settings\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect the \u003Cstrong\u003EExternal access\u003C/strong\u003E pane\u003C/li\u003E\u003Cli\u003EToggle on \u003Cstrong\u003EALLOW_ALL_ACCESS_INTEGRATION\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ESave\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ERestart your notebook session for the changes to take effect\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ENow you're ready to begin building your ML model with Ray for distributed processing.\u003C/p\u003E\n","\u003Ch2\u003EInitialize Ray for Distributed Processing\u003C/h2\u003E\n","\u003Cp\u003EIn this section, we'll set up Ray to enable unstructured data ingestion and distributed processing capabilities in Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EConfigure Ray Environment\u003C/h3\u003E\n","\u003Cp\u003EOpen a Snowflake Notebook and initialize the environment by importing Ray and configuring logging to suppress verbose output. Set up the Ray data context and disable progress bars for cleaner output. Import required libraries including Streamlit and pandas, and initialize the Snowpark session to interact with your Snowflake data.\u003C/p\u003E\n","\u003Ch3\u003EScale the Cluster\u003C/h3\u003E\n","\u003Cp\u003EConfigure the Ray cluster for better performance by scaling the computing resources. We'll set a scale factor of 2 to allocate adequate resources for our machine learning tasks. This ensures efficient processing of our review data and model training.\u003C/p\u003E\n","\u003Ch2\u003EProcess Review Text Data\u003C/h2\u003E\n","\u003Cp\u003ENow we'll implement the data processing pipeline to extract insights from customer reviews and prepare them for analysis.\u003C/p\u003E\n","\u003Ch3\u003ESet Up Data Source\u003C/h3\u003E\n","\u003Cp\u003EImport the SFStageTextDataSource class from snowflake.ml.ray.datasource and configure a data source pointing to review text files in your Snowflake stage. Set up wildcard pattern matching to read all text files and create a Ray dataset by reading from this data source. These files contain synthetic customer feedback across different product categories.\u003C/p\u003E\n","\u003Ch3\u003EParse Review Data\u003C/h3\u003E\n","\u003Cp\u003ECreate a parsing function that extracts UUID and the review text from each record. The function splits each text entry on a comma delimiter and cleans and formats the extracted fields by removing quotes and other characters. We'll return a dictionary with parsed fields and apply this function to the dataset using Ray's map operation. This structured review data will be crucial for understanding customer sentiment across different market segments.\u003C/p\u003E\n","\u003Ch2\u003EPredict Review Quality\u003C/h2\u003E\n","\u003Cp\u003EIn this step, we'll predict the quality of reviews using an open-source pre-trained model via a HuggingFace Pipeline to understand how review content impacts purchase decisions.\u003C/p\u003E\n","\u003Ch3\u003ECreate Model Predictor Class\u003C/h3\u003E\n","\u003Cp\u003EDefine a class for batch processing reviews and initialize a zero-shot classification pipeline using the facebook/bart-large-mnli model. Set up classification labels to categorize reviews by quality:\u003C/p\u003E\n","\u003Cp\u003E"Detailed with specific information and experience"\n"Basic accurate information"\n"Generic brief with no details"\u003C/p\u003E\n","\u003Cp\u003EImplement batch processing logic to handle multiple reviews efficiently and extract and format classification results to identify high-quality versus low-quality feedback.\u003C/p\u003E\n","\u003Cp\u003EProcess the entire dataset using Ray's distributed computing capabilities. Use map_batches to apply the model to groups of reviews, configure concurrency to match the cluster scale factor, set an appropriate batch size to optimize performance, and allocate CPU resources for efficient processing. This analysis helps identify which types of reviews are most influential in purchase decisions.\u003C/p\u003E\n","\u003Ch2\u003EStore Processed Data in Snowflake\u003C/h2\u003E\n","\u003Cp\u003ENow we'll save our processed data back to Snowflake tables for further analysis and model building.\u003C/p\u003E\n","\u003Ch3\u003ECreate Data Sink\u003C/h3\u003E\n","\u003Cp\u003EImport SnowflakeTableDatasink from snowflake.ml.ray.datasink and configure the data sink to write to a table named "REVIEWS". Enable auto-create table option to automatically generate the table structure and set override parameter to false to prevent accidental data loss. Write the processed dataset to the configured data sink. This creates a centralized repository of analyzed customer feedback that can be joined with other datasets.\u003C/p\u003E\n","\u003Ch3\u003EVerify Results\u003C/h3\u003E\n","\u003Cp\u003EExamine the first few rows of processed data using a simple SQL query to confirm the data was successfully stored, including the review quality classifications.\u003C/p\u003E\n","\u003Ch2\u003EAdd Sentiment Analysis\u003C/h2\u003E\n","\u003Cp\u003EIn this step, we use Snowflake Cortex to enhance our reviews with sentiment analysis, providing additional insights for our model.\u003C/p\u003E\n","\u003Ch3\u003ESentiment Analysis Process\u003C/h3\u003E\n","\u003Cp\u003EThe notebook performs these key operations:\u003C/p\u003E\n","\u003Cp\u003EAdds a REVIEW_SENTIMENT column to the REVIEWS table to store sentiment scores\nUses the Snowflake Cortex SENTIMENT function to analyze the emotional tone of each review\nUpdates the table with sentiment scores ranging from -1 (negative) to 1 (positive)\u003C/p\u003E\n","\u003Cp\u003EThis enriches our dataset with quantified emotional data that serves as a critical feature in predictive modeling. Unlike the review quality assessment (which uses our custom ML model), the sentiment analysis is performed using Snowflake's built-in Cortex capabilities, demonstrating how to combine custom ML processing with Snowflake's native AI functions for comprehensive analysis.\u003C/p\u003E\n","\u003Ch2\u003EPrepare Data for Model Training\u003C/h2\u003E\n","\u003Cp\u003ENow we'll join our review data with website performance metrics to prepare for model training, creating a comprehensive view of the customer journey.\u003C/p\u003E\n","\u003Ch3\u003EJoin Tables\u003C/h3\u003E\n","\u003Cp\u003EImport the DataConnector from snowflake.ml.data, load tabular data (containing page load times, product layouts, and other metrics) and review data, and join the tables using UUID and REVIEW_ID as the key. Use an inner join to ensure all records have matching entries. This creates a holistic view of both technical performance and customer sentiment data.\u003C/p\u003E\n","\u003Ch3\u003EExamine the Combined Dataset\u003C/h3\u003E\n","\u003Cp\u003ECheck the dataset to ensure it's ready for modeling by counting the total number of rows after joining, listing all available columns to identify features, and confirming that we have both technical metrics (page load time) and sentiment data. This combined dataset allows us to analyze the relative impact of technical performance versus product perception.\u003C/p\u003E\n","\u003Ch3\u003EFeature Engineering\u003C/h3\u003E\n","\u003Cp\u003EPrepare categorical features for model training by importing LabelEncoder from snowflake.ml.modeling.preprocessing, identifying categorical columns that need encoding (REVIEW_QUALITY, PRODUCT_LAYOUT), creating encoders for each target column, and applying the fit and transform pattern to convert categories to numeric values. This ensures all data is in a format suitable for our predictive model.\u003C/p\u003E\n","\u003Ch2\u003ETrain an XGBoost Model\u003C/h2\u003E\n","\u003Cp\u003EIn this step, we'll use a Snowflake distributed XGBEstimator to train an XGBoost model to predict purchase decisions based on both technical and sentiment factors.\u003C/p\u003E\n","\u003Ch3\u003EConfigure Model Parameters\u003C/h3\u003E\n","\u003Cp\u003EImport XGBEstimator and XGBScalingConfig from snowflake.ml.modeling.distributors.xgboost and identify input features from the prepared dataset:\u003C/p\u003E\n","\u003Cp\u003EREVIEW_QUALITY_OUT (encoded review quality from our custom model)\nPRODUCT_LAYOUT_OUT (encoded layout type)\nPAGE_LOAD_TIME (technical performance)\nREVIEW_SENTIMENT (customer satisfaction from Cortex AI)\nPRODUCT_RATING (product quality)\u003C/p\u003E\n","\u003Cp\u003ESpecify the target column (PURCHASE_DECISION) and configure hyperparameters for the XGBoost model including learning rate, tree depth, and minimum child weight. Set up scaling configuration without GPU acceleration.\u003C/p\u003E\n","\u003Ch3\u003EInitialize and Train Model\u003C/h3\u003E\n","\u003Cp\u003ECreate the XGBEstimator with the configured parameters, convert the training dataframe to a compatible format using DataConnector, and fit the model on the training data using the selected input columns and label. The training process runs distributed across the Ray cluster. This model will help identify which factors have the greatest influence on purchase likelihood.\u003C/p\u003E\n","\u003Ch2\u003ERegister and Deploy the Model\u003C/h2\u003E\n","\u003Cp\u003EIn this section, we'll register our model in Snowflake's model registry to make it available for business applications.\u003C/p\u003E\n","\u003Ch3\u003ERegister Model\u003C/h3\u003E\n","\u003Cp\u003EImport the registry module from snowflake.ml.registry and initialize a Registry object connected to the current session. Log the trained model to the Snowflake Model Registry with the name "deployed_xgb" and include necessary dependencies for runtime execution (scikit-learn, xgboost). Provide sample input data for validation, add descriptive metadata including a comment about the model's purpose, enable explainability features for model interpretation, and configure deployment targets for warehouse execution. This makes the model available for business stakeholders to use in decision-making.\u003C/p\u003E\n","\u003Ch3\u003EGenerate Model Explanations\u003C/h3\u003E\n","\u003Cp\u003ERun the model's explain function on input data and review the generated explanations to understand feature importance. This reveals the relative importance of technical metrics versus product perception in driving purchases. These insights can help businesses decide whether to allocate resources to technical optimization or product improvement.\u003C/p\u003E\n","\u003Ch2\u003ECreate Automated ML Pipeline\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E You will not be able to do these next steps of the Notebook on a Trial Account\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ENow we'll set up an automated workflow using ML Jobs to run remote code, and Snowflake's DAG framework to process new reviews and retrain models.\u003C/p\u003E\n","\u003Ch3\u003EDefine Remote Jobs\u003C/h3\u003E\n","\u003Cp\u003EFirst, create a job to process new reviews by importing the remote decorator from snowflake.ml.jobs, creating a function that processes new reviews using the pipeline we built, applying the remote decorator to enable Snowflake to run this function as a job, and configuring the compute pool, stage, and access integrations.\u003C/p\u003E\n","\u003Cp\u003ENext, create a job for model retraining that reproduces our feature engineering, model training, and model registration steps, with added versioning to track model updates over time.\u003C/p\u003E\n","\u003Ch3\u003ECreate DAG for Workflow Orchestration\u003C/h3\u003E\n","\u003Cp\u003EBuild an automated workflow using Snowflake's DAG framework by defining the workflow structure, scheduling parameters, and stage locations. Create tasks for each step in the workflow (setup, review processing, sentiment analysis, model training) and connect them in sequence using the directed acyclic graph structure.\u003C/p\u003E\n","\u003Ch3\u003EDeploy and Run the DAG\u003C/h3\u003E\n","\u003Cp\u003EDeploy the defined workflow to your Snowflake account, run the workflow to verify functionality, and monitor execution status to track progress of the deployment. This automation ensures that your ML pipeline runs regularly, keeping your predictions current with the latest data.\u003C/p\u003E\n","\u003Ch2\u003ESetting Up the Streamlit App\u003C/h2\u003E\n","\u003Cp\u003ETo create and configure your Streamlit application in Snowflake:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to Streamlit in Snowflake:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on the \u003Cstrong\u003EStreamlit\u003C/strong\u003E tab in the left navigation pane\u003C/li\u003E\u003Cli\u003EClick on \u003Cstrong\u003E+ Streamlit App\u003C/strong\u003E button in the top right\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EConfigure App Settings:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EEnter "Purchase Decision Dashboard" as your app name\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EHOL_WAREHOUSE\u003C/strong\u003E as your warehouse\u003C/li\u003E\u003Cli\u003EChoose HOL_DB as your database and HOL_SCHEMA as your schema\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECreate the app:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIn the editor, paste the complete code from your downloaded \u003Ccode\u003Estreamlit.py\u003C/code\u003E file\u003C/li\u003E\u003Cli\u003EClick "Run" to launch your application\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYour Streamlit app will create visualizations showing the relationship between review quality, sentiment, and purchase decisions, helping stakeholders understand key factors in the customer journey.\u003C/p\u003E\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Ch3\u003EConclusion\u003C/h3\u003E\n","\u003Cp\u003ECongratulations! You've successfully built an end-to-end ML workflow in Snowflake ML that processes customer reviews, analyzes sentiment with gen AI, and predicts purchase decisions using XGBoost. You've also created an automated pipeline that handles data processing and model retraining, deployed in the Snowflake ecosystem. This solution shows how to identify which factors have the greatest influence on purchase decisions, helping businesses decide where to allocate resources - whether on improving website performance or enhancing product quality and customer satisfaction.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to pipeline unstructured data into your Snowflake workflows\u003C/li\u003E\u003Cli\u003EHow to use Ray for scalable distributed data processing\u003C/li\u003E\u003Cli\u003EHow to process synthetic customer feedback data utilizing open source LLMs for classification and Cortex AI for sentiment analysis\u003C/li\u003E\u003Cli\u003EHow to use custom ML models to assess review quality and helpfulness and train across multiple nodes\u003C/li\u003E\u003Cli\u003EHow to deploy the end to end pipeline to production using MLJobs and Snowflake Tasks\u003C/li\u003E\u003Cli\u003EHow to evaluate the relative importance of user experience features on purchase decisions\u003C/li\u003E\u003Cli\u003EHow to create dynamic visualizations to communicate insights to stakeholders\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"/en/data-cloud/snowflake-ml/\"\u003ESnowflake ML Webpage\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-ml\"\u003EDocumentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/\"\u003EQuickstart: Build an End-to-End ML Model in Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/\"\u003EOther Related Quickstarts\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EHave a question? Ask in \u003Ca href=\"https://community.snowflake.com/\"\u003ESnowflake Community\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Build ML models to predict customer conversions in Snowflake for marketing optimization and conversion rate improvement.","title":"Building ML Models to Crack the Code of Customer Conversions","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview \n\nWill they buy? This hands-on lab will guide you through building a custom machine learning model to predict customer purchase likelihood by analyzing the complex relationship between website experience metrics and product reviews. Using Snowflake's advanced gen AI and machine learning capabilities, you'll work with a carefully constructed synthetic dataset that simulates real-world scenarios, allowing for controlled exploration of various factors affecting purchase decisions.\n\n### What You'll Build\n- A complete ML pipeline that processes and classifies text data, performs sentiment analysis, and builds a predictive model \n- A horizontally scalable system to extract insights from customer reviews using both custom processing with OSS LLMs directly and Snowflake Cortex AI\n- An automated workflow to deploy continuous data processing on a scheduled basis\n- A dashboard to visualize the impact of user experience features on purchase decisions\n\n### What You'll Learn\n- How to pipeline unstructured data into your Snowflake workflows\n- How to use Ray for scalable distributed data processing\n- How to process synthetic customer feedback data utilizing open source LLMs for classification and Cortex AI for sentiment analysis\n- How to use custom ML models to assess review quality and helpfulness and train across multiple nodes\n- How to deploy the end to end pipeline to production using ML Jobs and Snowflake Tasks\n- How to evaluate the relative importance of user experience features on purchase decisions\n- How to create dynamic visualizations to communicate insights to stakeholders\n\n### Prerequisites\n- A Snowflake Account. [Sign up for a 30-day free trial account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides), make sure to select AWS WEST\n- Familiarity with Python and ML concepts\n\n## Setup Environment\n\nBefore diving into Ray and distributed processing, let's set up our environment with the necessary database objects and sample data.\n\n### Download Setup Files\n\n1. Download these three files to your local machine:\n   * [setup.sql](https://github.com/Snowflake-Labs/sfguide-build-ml-models-for-customer-conversions/blob/main/setup.sql)\n   * [notebook.ipynb](https://github.com/Snowflake-Labs/sfguide-build-ml-models-for-customer-conversions/blob/main/notebook.ipynb)\n   * [streamlit.py](https://github.com/Snowflake-Labs/sfguide-build-ml-models-for-customer-conversions/blob/main/streamlit.py)\n\n### Run the Setup Script\n\n1. Log in to your Snowflake account\n2. Navigate to Worksheets:\n   * Click on the **Worksheets** tab in the left navigation pane\n   * Click on the **+** button to create a new worksheet\n\n3. Execute the setup script:\n   * Open the worksheet editor\n   * Copy and paste the contents from your downloaded `setup.sql` file\n   * Click the **Run All** button to execute the script\n\nThe setup script creates the necessary database, schema, tables, and loads sample data for our analysis.\n\n\u003E \n\u003E `CREATE COMPUTE POOL` step is expected to fail on trial accounts \n\n### Import the Notebook\n\n1. Navigate to Snowflake Notebooks:\n   * Click on **Projects** » **Notebooks** in the left navigation menu\n\n2. Import the notebook file:\n   * Click on the **...** menu in the top right\n   * Select **Import .ipynb file** from the dropdown\n   * Select the `notebook.ipynb` file you downloaded earlier\n\n3. Configure the notebook:\n   * For **Database**, select `HOL_DB`\n   * For **Schema**, select `HOL_SCHEMA`\n   * For **Runtime**, select `Run on Container`  \n   * For **Runtime version**, Leave as is \n   * For **Compute pool**, Leave as is\n   * For **Query warehouse**, select `HOL_WAREHOUSE`\n   * Click **Create**\n\n### Enable External Access Integrations\n\nAfter creating the notebook, you'll need to enable external access for internet connectivity:\n\n1. In your newly created notebook, click on the three dots (**...**) in the top right corner\n2. Select **Notebook settings**\n3. Select the **External access** pane\n4. Toggle on **ALLOW_ALL_ACCESS_INTEGRATION**\n5. Click **Save**\n6. Restart your notebook session for the changes to take effect\n\nNow you're ready to begin building your ML model with Ray for distributed processing.\n\n## Initialize Ray for Distributed Processing\n\nIn this section, we'll set up Ray to enable unstructured data ingestion and distributed processing capabilities in Snowflake.\n\n### Configure Ray Environment\nOpen a Snowflake Notebook and initialize the environment by importing Ray and configuring logging to suppress verbose output. Set up the Ray data context and disable progress bars for cleaner output. Import required libraries including Streamlit and pandas, and initialize the Snowpark session to interact with your Snowflake data.\n\n### Scale the Cluster\nConfigure the Ray cluster for better performance by scaling the computing resources. We'll set a scale factor of 2 to allocate adequate resources for our machine learning tasks. This ensures efficient processing of our review data and model training.\n\n## Process Review Text Data\n\nNow we'll implement the data processing pipeline to extract insights from customer reviews and prepare them for analysis.\n\n### Set Up Data Source\nImport the SFStageTextDataSource class from snowflake.ml.ray.datasource and configure a data source pointing to review text files in your Snowflake stage. Set up wildcard pattern matching to read all text files and create a Ray dataset by reading from this data source. These files contain synthetic customer feedback across different product categories.\n\n### Parse Review Data\nCreate a parsing function that extracts UUID and the review text from each record. The function splits each text entry on a comma delimiter and cleans and formats the extracted fields by removing quotes and other characters. We'll return a dictionary with parsed fields and apply this function to the dataset using Ray's map operation. This structured review data will be crucial for understanding customer sentiment across different market segments.\n\n## Predict Review Quality\n\nIn this step, we'll predict the quality of reviews using an open-source pre-trained model via a HuggingFace Pipeline to understand how review content impacts purchase decisions.\n\n### Create Model Predictor Class\nDefine a class for batch processing reviews and initialize a zero-shot classification pipeline using the facebook/bart-large-mnli model. Set up classification labels to categorize reviews by quality:\n\n\"Detailed with specific information and experience\"\n\"Basic accurate information\"\n\"Generic brief with no details\"\n\nImplement batch processing logic to handle multiple reviews efficiently and extract and format classification results to identify high-quality versus low-quality feedback.\n\nProcess the entire dataset using Ray's distributed computing capabilities. Use map_batches to apply the model to groups of reviews, configure concurrency to match the cluster scale factor, set an appropriate batch size to optimize performance, and allocate CPU resources for efficient processing. This analysis helps identify which types of reviews are most influential in purchase decisions.\n\n## Store Processed Data in Snowflake\n\nNow we'll save our processed data back to Snowflake tables for further analysis and model building.\n\n### Create Data Sink\nImport SnowflakeTableDatasink from snowflake.ml.ray.datasink and configure the data sink to write to a table named \"REVIEWS\". Enable auto-create table option to automatically generate the table structure and set override parameter to false to prevent accidental data loss. Write the processed dataset to the configured data sink. This creates a centralized repository of analyzed customer feedback that can be joined with other datasets.\n\n### Verify Results\nExamine the first few rows of processed data using a simple SQL query to confirm the data was successfully stored, including the review quality classifications.\n\n## Add Sentiment Analysis\n\nIn this step, we use Snowflake Cortex to enhance our reviews with sentiment analysis, providing additional insights for our model.\n\n### Sentiment Analysis Process\nThe notebook performs these key operations:\n\nAdds a REVIEW_SENTIMENT column to the REVIEWS table to store sentiment scores\nUses the Snowflake Cortex SENTIMENT function to analyze the emotional tone of each review\nUpdates the table with sentiment scores ranging from -1 (negative) to 1 (positive)\n\nThis enriches our dataset with quantified emotional data that serves as a critical feature in predictive modeling. Unlike the review quality assessment (which uses our custom ML model), the sentiment analysis is performed using Snowflake's built-in Cortex capabilities, demonstrating how to combine custom ML processing with Snowflake's native AI functions for comprehensive analysis.\n\n## Prepare Data for Model Training\n\nNow we'll join our review data with website performance metrics to prepare for model training, creating a comprehensive view of the customer journey.\n\n### Join Tables\nImport the DataConnector from snowflake.ml.data, load tabular data (containing page load times, product layouts, and other metrics) and review data, and join the tables using UUID and REVIEW_ID as the key. Use an inner join to ensure all records have matching entries. This creates a holistic view of both technical performance and customer sentiment data.\n\n### Examine the Combined Dataset\nCheck the dataset to ensure it's ready for modeling by counting the total number of rows after joining, listing all available columns to identify features, and confirming that we have both technical metrics (page load time) and sentiment data. This combined dataset allows us to analyze the relative impact of technical performance versus product perception.\n\n### Feature Engineering\nPrepare categorical features for model training by importing LabelEncoder from snowflake.ml.modeling.preprocessing, identifying categorical columns that need encoding (REVIEW_QUALITY, PRODUCT_LAYOUT), creating encoders for each target column, and applying the fit and transform pattern to convert categories to numeric values. This ensures all data is in a format suitable for our predictive model.\n\n## Train an XGBoost Model\n\nIn this step, we'll use a Snowflake distributed XGBEstimator to train an XGBoost model to predict purchase decisions based on both technical and sentiment factors.\n\n### Configure Model Parameters\nImport XGBEstimator and XGBScalingConfig from snowflake.ml.modeling.distributors.xgboost and identify input features from the prepared dataset:\n\nREVIEW_QUALITY_OUT (encoded review quality from our custom model)\nPRODUCT_LAYOUT_OUT (encoded layout type)\nPAGE_LOAD_TIME (technical performance)\nREVIEW_SENTIMENT (customer satisfaction from Cortex AI)\nPRODUCT_RATING (product quality)\n\nSpecify the target column (PURCHASE_DECISION) and configure hyperparameters for the XGBoost model including learning rate, tree depth, and minimum child weight. Set up scaling configuration without GPU acceleration.\n\n### Initialize and Train Model\nCreate the XGBEstimator with the configured parameters, convert the training dataframe to a compatible format using DataConnector, and fit the model on the training data using the selected input columns and label. The training process runs distributed across the Ray cluster. This model will help identify which factors have the greatest influence on purchase likelihood.\n\n## Register and Deploy the Model\n\nIn this section, we'll register our model in Snowflake's model registry to make it available for business applications.\n\n### Register Model\nImport the registry module from snowflake.ml.registry and initialize a Registry object connected to the current session. Log the trained model to the Snowflake Model Registry with the name \"deployed_xgb\" and include necessary dependencies for runtime execution (scikit-learn, xgboost). Provide sample input data for validation, add descriptive metadata including a comment about the model's purpose, enable explainability features for model interpretation, and configure deployment targets for warehouse execution. This makes the model available for business stakeholders to use in decision-making.\n\n### Generate Model Explanations\nRun the model's explain function on input data and review the generated explanations to understand feature importance. This reveals the relative importance of technical metrics versus product perception in driving purchases. These insights can help businesses decide whether to allocate resources to technical optimization or product improvement.\n\n## Create Automated ML Pipeline\n\n\u003E \n\u003E **Note** You will not be able to do these next steps of the Notebook on a Trial Account\n\nNow we'll set up an automated workflow using ML Jobs to run remote code, and Snowflake's DAG framework to process new reviews and retrain models.\n\n### Define Remote Jobs\nFirst, create a job to process new reviews by importing the remote decorator from snowflake.ml.jobs, creating a function that processes new reviews using the pipeline we built, applying the remote decorator to enable Snowflake to run this function as a job, and configuring the compute pool, stage, and access integrations.\n\nNext, create a job for model retraining that reproduces our feature engineering, model training, and model registration steps, with added versioning to track model updates over time.\n\n### Create DAG for Workflow Orchestration\nBuild an automated workflow using Snowflake's DAG framework by defining the workflow structure, scheduling parameters, and stage locations. Create tasks for each step in the workflow (setup, review processing, sentiment analysis, model training) and connect them in sequence using the directed acyclic graph structure.\n\n### Deploy and Run the DAG\nDeploy the defined workflow to your Snowflake account, run the workflow to verify functionality, and monitor execution status to track progress of the deployment. This automation ensures that your ML pipeline runs regularly, keeping your predictions current with the latest data.\n\n## Setting Up the Streamlit App\n\nTo create and configure your Streamlit application in Snowflake:\n\n1. Navigate to Streamlit in Snowflake:\n   * Click on the **Streamlit** tab in the left navigation pane\n   * Click on **+ Streamlit App** button in the top right\n\n2. Configure App Settings:\n   * Enter \"Purchase Decision Dashboard\" as your app name\n   * Select **HOL_WAREHOUSE** as your warehouse\n   * Choose HOL_DB as your database and HOL_SCHEMA as your schema\n\n3. Create the app:\n   * In the editor, paste the complete code from your downloaded `streamlit.py` file\n   * Click \"Run\" to launch your application\n\nYour Streamlit app will create visualizations showing the relationship between review quality, sentiment, and purchase decisions, helping stakeholders understand key factors in the customer journey.\n\n## Conclusion and Resources\n\n### Conclusion\nCongratulations! You've successfully built an end-to-end ML workflow in Snowflake ML that processes customer reviews, analyzes sentiment with gen AI, and predicts purchase decisions using XGBoost. You've also created an automated pipeline that handles data processing and model retraining, deployed in the Snowflake ecosystem. This solution shows how to identify which factors have the greatest influence on purchase decisions, helping businesses decide where to allocate resources - whether on improving website performance or enhancing product quality and customer satisfaction.\n\n### What You Learned\n- How to pipeline unstructured data into your Snowflake workflows\n- How to use Ray for scalable distributed data processing\n- How to process synthetic customer feedback data utilizing open source LLMs for classification and Cortex AI for sentiment analysis\n- How to use custom ML models to assess review quality and helpfulness and train across multiple nodes\n- How to deploy the end to end pipeline to production using MLJobs and Snowflake Tasks\n- How to evaluate the relative importance of user experience features on purchase decisions\n- How to create dynamic visualizations to communicate insights to stakeholders\n\n### Resources\n- [Snowflake ML Webpage](/en/data-cloud/snowflake-ml/)\n- [Documentation](https://docs.snowflake.com/en/user-guide/snowflake-ml)\n- [Quickstart: Build an End-to-End ML Model in Snowflake](/en/developers/guides/)\n- [Other Related Quickstarts](/en/developers/guides/)\n- Have a question? 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