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------------------------ -->\n\u003Cblockquote\u003E\n","\u003Cp\u003E🎥 \u003Cstrong\u003EPrefer a guided walkthrough?\u003C/strong\u003E Follow along in the \u003Ca href=\"https://www.snowflake.com/en/webinars/virtual-hands-on-lab/build-your-first-agentic-ml-pipeline-with-natural-language-2026-05-28/\"\u003Evirtual hands-on lab\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch2\u003E1. Overview\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"http://www.snowflake.com/ml\"\u003ESnowflake ML\u003C/a\u003E is changing how teams work with agentic ML, an autonomous, reasoning-based system that enables developers to use agents to plan and execute tasks across the entire ML pipeline. In this quickstart, learn how to build and run a customer lifetime value (LTV) prediction model with only a handful of prompts so that you can go from raw data to production predictions in minutes, not weeks, with \u003Ca href=\"https://www.snowflake.com/en/product/features/cortex-code/\"\u003ECortex Code\u003C/a\u003E, Snowflake's AI native coding agent. Cortex Code is available both as a CLI and directly in Snowsight, Snowflake's web interface.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E Cortex Code is powered by LLMs and is non-deterministic. The code it generates may differ from what is shown in this guide. Always review the output and verify that the results match your expectations before proceeding to the next step.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EGenerate realistic synthetic e-commerce data with natural language prompts\u003C/li\u003E\u003Cli\u003EPerform exploratory data analysis and feature engineering conversationally\u003C/li\u003E\u003Cli\u003ETrain and compare multiple regression models inside Snowflake\u003C/li\u003E\u003Cli\u003ELog models with metrics to the Snowflake Model Registry\u003C/li\u003E\u003Cli\u003ERun batch inference on a Snowflake Warehouse\u003C/li\u003E\u003Cli\u003E(Optional) Deploy a model as a REST API on Snowpark Container Services (SPCS) for real-time inference\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EA complete customer LTV prediction pipeline featuring:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESynthetic e-commerce transactions dataset (~500 customers, ~100,000 transactions over 18 months)\u003C/li\u003E\u003Cli\u003ETrained regression model predicting a customer's total spend in the next 90 days\u003C/li\u003E\u003Cli\u003ERegistered model in the Snowflake Model Registry with evaluation metrics\u003C/li\u003E\u003Cli\u003EBatch inference predictions via Snowflake Warehouse\u003C/li\u003E\u003Cli\u003E(Optional) A real-time inference REST endpoint on SPCS with latency profiling\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESign up for the 30-day \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003Efree trial\u003C/a\u003E of Snowflake. Have \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role or a role with permissions to create databases, schemas, tables, and models\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-snowsight\"\u003ECortex Code in Snowsight\u003C/a\u003E (no local installation required)\u003C/li\u003E\u003Cli\u003EA dedicated Snowflake warehouse\u003C/li\u003E\u003Cli\u003E(Optional for SPCS) A compute pool configured for Snowpark Container Services — see the \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/common-setup\"\u003Eofficial setup guide\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EFamiliarity with basic ML concepts (training, evaluation, inference)\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsing Cortex Code CLI?\u003C/strong\u003E The same prompts work in both interfaces. See \u003Ca href=\"#cortex-code-cli-walkthrough\"\u003ECortex Code CLI Walkthrough\u003C/a\u003E for CLI-specific setup and terminal output examples.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E2. Setup\u003C/h2\u003E\n","\u003Ch3\u003ECortex Code in Snowsight\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ECortex Code\u003C/a\u003E is an AI agent built into Snowflake, designed for data engineering, analytics, ML, and agent-building tasks. It operates autonomously within your Snowflake environment, leveraging deep knowledge of RBAC, schemas, and platform best practices.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EOpen a Workspace Notebook by going to the sidebar and clicking on Projects > Workspaces; then in the "My Workspace" panel, click on "+ Add new" > Notebook.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOnce the notebook loads, look for Cortex Code in the lower-right corner of Snowsight.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: Cortex Code is environment aware so using it in a Workspace Notebook will give the best results as it will have access to all the tools provided by the notebook. When relevant, generated code will be inserted into the notebook and run on your behalf.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EYou are now ready to start prompting Cortex Code to build your ML pipeline.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E3. Generate Synthetic Data\u003C/h2\u003E\n","\u003Cp\u003EFirst, let's create the database objects and generate synthetic e-commerce transaction data using Cortex Code.\u003C/p\u003E\n","\u003Ch3\u003EPrompt\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003EGenerate realistic looking synthetic data in database COCO_DB and schema COCO_SCHEMA \n(create if it doesn't exist). Create a table ML_LTV_TRANSACTIONS\nwith ~100000 transactions from ~500 customers over an 18-month period. Include\nCUSTOMER_ID, TRANSACTION_TIME, AMOUNT, PRODUCT_CATEGORY, and CHANNEL. Make the\ndata realistic: customers should have varying purchase frequencies (some buy\nweekly, others monthly), amounts should vary by category (electronics $50-$2000,\ngroceries $10-$200, apparel $20-$500), and channels should be web, mobile, or\nin-store. About 10% of customers should be high-value (frequent buyers with\nhigher average spend).\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat Gets Generated\u003C/h3\u003E\n","\u003Cp\u003EEnter the prompt in the Cortex Code chat panel. Cortex Code analyzes the request and breaks it into a multi-step plan:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-code-in-snowsight-for-data-science-ml/generate-synthetic-data-by-cortex-code.jpg?v=575b6e9d\" alt=\"Generate synthetic data by Cortex Code in Snowsight\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECortex Code generates the SQL or Python code to create the database objects and populate the table, then executes it automatically. You will see the code and results appear in a new Notebook cell:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-code-in-snowsight-for-data-science-ml/show-basic-statistics-by-cortex-code.jpg?v=575b6e9d\" alt=\"Basic statistics by Cortex Code in Snowsight\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: Due to the inherent randomness in how LLMs generate text, your results may vary slightly from what is shown in this tutorial.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ETo verify the data was generated correctly, run the following SQL in a Snowsight worksheet:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: \u003Ccode\u003ECOCO_DB.COCO_SCHEMA\u003C/code\u003E is an example database and schema. If Cortex Code saved the data to a different database or schema in your environment, update these values before running the query.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM COCO_DB.COCO_SCHEMA.ML_LTV_TRANSACTIONS LIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see 10 rows with columns like \u003Ccode\u003ECUSTOMER_ID\u003C/code\u003E, \u003Ccode\u003ETRANSACTION_TIME\u003C/code\u003E, \u003Ccode\u003EAMOUNT\u003C/code\u003E, \u003Ccode\u003EPRODUCT_CATEGORY\u003C/code\u003E, and \u003Ccode\u003ECHANNEL\u003C/code\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAlternative:\u003C/strong\u003E If you prefer to load a pre-built dataset instead of generating data, see \u003Ca href=\"#appendix-a-load-pre-built-dataset-from-s3\"\u003EAppendix A — Load Pre-Built Dataset from S3\u003C/a\u003E at the end of this guide.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E4. Explore the Data (EDA)\u003C/h2\u003E\n","\u003Cp\u003EBefore training a model, analyze patterns to identify the right features for predicting customer lifetime value.\u003C/p\u003E\n","\u003Ch3\u003EPrompt\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003EDo exploratory data analysis and recommend the features needed to train a regression model that can predict each customer's total spend in the next 90 days.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat Gets Generated\u003C/h3\u003E\n","\u003Cp\u003ECortex Code first verifies the table and shows a summary (row count, customer count, date range, and category breakdown), then performs deeper analysis across multiple steps — purchase frequency, spending distributions, recency patterns, and category preferences — and summarizes key findings with recommended features.\u003C/p\u003E\n","\u003Cp\u003EIf the table is empty (or missing), see \u003Ca href=\"#appendix-a-load-pre-built-dataset-from-s3\"\u003EAppendix A\u003C/a\u003E to load the pre-built dataset and retry.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-code-in-snowsight-for-data-science-ml/snowsight-cortex-code-eda-feature-recommendations.jpg?v=575b6e9d\" alt=\"EDA results and feature recommendations in Cortex Code\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn this example, Cortex Code identifies signals such as purchase frequency trends, average order value by customer segment, recency of last purchase, and preferred product categories. These insights translate into features like total_transactions, avg_amount, days_since_last_purchase, favorite_category, and channel_distribution.\u003C/p\u003E\n","\u003Cp\u003EThe EDA step typically reveals patterns such as:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EHigh-value customers purchase more frequently and have higher average order values\u003C/li\u003E\u003Cli\u003ERecency of last purchase is a strong predictor of future spend\u003C/li\u003E\u003Cli\u003ECertain product categories correlate with higher lifetime value\u003C/li\u003E\u003Cli\u003EChannel preferences (web vs. mobile vs. in-store) vary across customer segments\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E5. Train the Model\u003C/h2\u003E\n","\u003Cp\u003EWith our features identified, we can now train a regression model. XGBoost and Random Forest are excellent choices for this kind of tabular prediction task.\u003C/p\u003E\n","\u003Ch3\u003EPrompt\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003EBuild those features and train a regression model to predict each customer's total spend in the next 90 days. Use two different algorithms, XGBoost and Random Forest, and evaluate the best one. Use 20% of the data as the eval set.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat Gets Generated\u003C/h3\u003E\n","\u003Cp\u003ECortex Code typically creates a Notebook, generates feature engineering steps, trains two models, and reports evaluation metrics so you can choose the best performer.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-code-in-snowsight-for-data-science-ml/snowsight-cortex-code-train-two-models.jpg?v=575b6e9d\" alt=\"Training and evaluation workflow created by Cortex Code\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn this example, Cortex Code generates Python for feature engineering (aggregating per-customer metrics from the transaction history), runs training/evaluation steps, and produces a comparison section (metrics like RMSE, MAE, and R-squared) to help you pick the best model.\u003C/p\u003E\n","\u003Cp\u003ECortex Code will:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EEngineer the features based on the EDA recommendations (per-customer aggregations over the training window)\u003C/li\u003E\u003Cli\u003ESplit the data into training (80%) and evaluation (20%) sets\u003C/li\u003E\u003Cli\u003ETrain two different regression algorithms (e.g., XGBoost and Random Forest)\u003C/li\u003E\u003Cli\u003ECompare their performance using metrics such as RMSE, MAE, and R-squared\u003C/li\u003E\u003Cli\u003ERecommend the better-performing model\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EReview the evaluation metrics to confirm the model meets your requirements before proceeding.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E6. Log to Model Registry and Run Inference\u003C/h2\u003E\n","\u003Cp\u003ENow register the better model to the Snowflake Model Registry and run batch inference.\u003C/p\u003E\n","\u003Ch3\u003EPrompt\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003ELog the better model with metrics into Snowflake Model Registry, and use Snowflake Warehouse for inference.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex Code handles the \u003Ccode\u003Elog_model()\u003C/code\u003E call with appropriate parameters including model metrics, sample input for schema inference, and the target platform set to \u003Ccode\u003EWAREHOUSE\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-code-in-snowsight-for-data-science-ml/snowsight-cortex-code-model-registry-log-model.jpg?v=575b6e9d\" alt=\"Model logged to Snowflake Model Registry with metrics\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThen generate predictions:\u003C/p\u003E\n","\u003Ch3\u003EPrompt\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003ECreate feature profiles for 50 customers and run LTV predictions for them. Show the top 10 highest predicted LTV customers.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex Code generates the customer feature profiles, runs inference via your Snowflake Warehouse, and displays the predicted 90-day spend for each customer (sorted by highest predicted LTV).\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-code-in-snowsight-for-data-science-ml/snowsight-cortex-code-batch-inference-100-requests.jpg?v=575b6e9d\" alt=\"Batch inference results for LTV predictions\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOptional:\u003C/strong\u003E To deploy the model as a real-time REST endpoint on SPCS instead, see Appendix B — Real-Time Inference on SPCS at the end of this guide.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E7. Debug and Recover from Errors\u003C/h2\u003E\n","\u003Cp\u003EDuring any natural language coding session, errors are inevitable. The great thing about Cortex Code is its ability to self-correct by assessing the situation, environment, and error to fix issues automatically.\u003C/p\u003E\n","\u003Ch3\u003ECommon Scenarios\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EModel Registration Errors\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EWhen \u003Ccode\u003Elog_model()\u003C/code\u003E fails due to parameter issues (e.g., target platform mismatch), Cortex Code diagnoses the error and re-registers the model with corrected parameters automatically.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENotebook Execution Issues\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EWhen a cell fails due to missing imports or data type mismatches, Cortex Code detects the issue, adjusts the code, and re-executes the cell.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFeature Engineering Errors\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EIf a feature column is missing or a SQL view fails, Cortex Code investigates the schema, identifies the root cause, and regenerates the feature engineering step.\u003C/p\u003E\n","\u003Ch3\u003EBest Practices\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EStart with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E for initial setup, then create dedicated roles\u003C/li\u003E\u003Cli\u003EMonitor compute pool resources during SPCS deployment\u003C/li\u003E\u003Cli\u003EReview Cortex Code's explanations when it makes corrections\u003C/li\u003E\u003Cli\u003EUse the Snowsight Notebook environment for the best interactive experience with visualizations\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E8. Conclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully built a complete customer LTV prediction model using only a handful of natural language prompts in \u003Ca href=\"http://www.snowflake.com/ml\"\u003ESnowflake ML\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Built\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-code-in-snowsight-for-data-science-ml/architecture-diagram.svg?v=575b6e9d\" alt=\"LTV Prediction Pipeline Architecture\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EGenerate realistic synthetic e-commerce data with natural language prompts\u003C/li\u003E\u003Cli\u003EPerform comprehensive exploratory data analysis with automated feature recommendations\u003C/li\u003E\u003Cli\u003ETrain and compare multiple regression models for LTV prediction\u003C/li\u003E\u003Cli\u003ELog models with metrics to the Snowflake Model Registry\u003C/li\u003E\u003Cli\u003ERun batch inference on a Snowflake Warehouse\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003EWeb pages:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"http://www.snowflake.com/ml\"\u003ESnowflake ML\u003C/a\u003E - Integrated set of capabilities for development, MLOps and inference leading with agentic ML\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/notebooks/\"\u003ESnowflake Notebooks\u003C/a\u003E - Jupyter-based notebooks in Snowflake Workspaces\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/cortex-code/\"\u003ECortex Code\u003C/a\u003E - Snowflake's AI native coding agent that boosts ML productivity\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ETechnical Documentation:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/overview\"\u003ESnowflake ML Documentation\u003C/a\u003E - Official Snowflake ML developer guide\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/quickstart\"\u003ESnowflake ML Quickstart\u003C/a\u003E - Hands-on guides to get started with Snowflake ML\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ECortex Code Documentation\u003C/a\u003E - Getting started with Cortex Code\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-snowsight\"\u003ECortex Code in Snowsight\u003C/a\u003E - Browser-based experience\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli\"\u003ECortex Code CLI\u003C/a\u003E - Command-line experience\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview\"\u003ESnowflake Model Registry\u003C/a\u003E - Register, version, and deploy ML models\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview\"\u003ESnowpark Container Services\u003C/a\u003E - Deploy and manage containerized workloads\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOptional A - Cleanup\u003C/h2\u003E\n","\u003Cp\u003ETo avoid ongoing Snowflake credit consumption, you can clean up the resources created in this guide. There are two approaches: a \u003Cstrong\u003ECortex Code prompt\u003C/strong\u003E and \u003Cstrong\u003EManual SQL\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EIf you completed this quickstart in a single session and your environment does not contain other data, use "A-1 Cortex Code" prompt for a quick cleanup.\u003C/p\u003E\n","\u003Cp\u003EIf you worked through this quickstart over multiple days or your environment contains resources unrelated to this guide, use "A-2 Manual SQL" approach to ensure you only drop the intended objects.\u003C/p\u003E\n","\u003Ch3\u003EA-1. Cortex Code\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: This prompt works best within the same Cortex Code session where the database and other objects like model were created. If you are cleaning up resources from a previous session (e.g., a different day) or your environment contains objects unrelated to this quickstart, use the Manual SQL approach below for more precise control below.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003EDrop Database and model that we created earlier in this session\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex Code will generate and execute the appropriate DROP statements for each resource.\u003C/p\u003E\n","\u003Ch3\u003EA-2. Manual SQL\u003C/h3\u003E\n","\u003Cp\u003EIf you prefer to run the cleanup manually:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: \u003Ccode\u003ECOCO_DB.COCO_SCHEMA\u003C/code\u003E is an example database and schema, and \u003Ccode\u003ECOCO_WH\u003C/code\u003E is an example warehouse name. If Cortex Code saved the data to a different database or created a different warehouse in your environment, update these values before running the query.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Drop the database and all objects within it (table, schema, stage, etc.)\nDROP DATABASE IF EXISTS COCO_DB;\n\n-- Drop the model from the Model Registry\nDROP MODEL IF EXISTS COCO_DB.COCO_SCHEMA.ML_LTV_PREDICTOR;\n\n-- Drop the warehouse\nDROP WAREHOUSE IF EXISTS COCO_WH;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: \u003Ccode\u003EDROP DATABASE\u003C/code\u003E removes all schemas, tables, and stages inside it. Make sure you no longer need any of the data before running this command.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOptional B - Cortex Code CLI Walkthrough\u003C/h2\u003E\n","\u003Cp\u003EThe same prompts used throughout this guide work identically in Cortex Code CLI. This section shows CLI-specific setup and sample terminal output so you can compare what to expect in a terminal session.\u003C/p\u003E\n","\u003Ch3\u003ESetup\u003C/h3\u003E\n","\u003Cp\u003EInstall the CLI:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecurl -LsS https://ai.snowflake.com/static/cc-scripts/install.sh | sh\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter installing, run \u003Ccode\u003Ecortex\u003C/code\u003E and follow the setup wizard to connect to your Snowflake account. For detailed instructions, refer to the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli\"\u003ECortex Code CLI documentation\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EVerify your connection:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EWhat role am I using and what databases can I access?\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip\u003C/strong\u003E: Run Cortex Code CLI inside a VS Code or Cursor terminal to view generated files side-by-side.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip:\u003C/strong\u003E Prefix table names with \u003Ccode\u003E#\u003C/code\u003E (e.g., \u003Ccode\u003E#COCO_DB.COCO_SCHEMA.ML_LTV_TRANSACTIONS\u003C/code\u003E) to ground the conversation to a specific object.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EGenerate Synthetic Data — CLI Output\u003C/h3\u003E\n","\u003Cp\u003EAfter entering the Generate Synthetic Data prompt, Cortex Code CLI shows a summary like this:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E  Summary Statistics\n\n  ┌───────────┬──────────┐\n  │ Metric    │ Value    │\n  ├───────────┼──────────┤\n  │ Customers │ 500      │\n  ├───────────┼──────────┤\n  │ Min LTV   │ $12,930  │\n  ├───────────┼──────────┤\n  │ P25       │ $36,745  │\n  ├───────────┼──────────┤\n  │ Median    │ $54,617  │\n  ├───────────┼──────────┤\n  │ Mean      │ $76,769  │\n  ├───────────┼──────────┤\n  │ P75       │ $86,724  │\n  ├───────────┼──────────┤\n  │ Max       │ $495,857 │\n  ├───────────┼──────────┤\n  │ Std Dev   │ $67,972  │\n  └───────────┴──────────┘\n\n  Distribution (Histogram)\n\n    LTV Range           Customers\n    $12,930 - $36,868   126  █████████████████████████████████████\n    $37,086 - $61,126   170  ██████████████████████████████████████████████████\n    $61,382 - $84,439    74  ██████████████████████\n    $85,925 - $109,095   35  ██████████\n    $110,606 - $133,179  26  ████████\n    $134,850 - $157,594  18  █████\n    $158,039 - $181,760  19  ██████\n    $182,181 - $206,059   7  ██\n    $209,265 - $241,987  10  ███\n    $257,600 - $283,419   5  █\n    $345,815 - $387,939   7  ██\n    $410,296 - $495,857   3  █\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EExplore the Data (EDA) — CLI Output\u003C/h3\u003E\n","\u003Cp\u003ECortex Code CLI reads its built-in ML skill, detects the local Python environment, and runs the EDA in incremental steps:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E> Do exploratory data analysis and recommend the features needed to train a regression model that can predict each customer's total spend in the next 90 days.\n\n✓  SKILL  machine-learning\n\n✓  READ  cli-environment.md 194 lines\n\n✓  READ  ml-development/SKILL.md 279 lines\n\n* Let me set up the environment and build a comprehensive EDA script.\n\n✓  BASH  which uv\n  └─ /Users/shtanaka/.local/bin/uv\n\n✓  BASH  cortex env detect\n  └─ {"directory": "/Users/shtanaka/project/sfquickstarts", ...}\n\n✓  BASH  uv venv /Users/shtanaka/project/sfquickstarts/.venv --python 3.10 2>&1\n  └─ Creating virtual environment at: .venv\n\n✓  BASH  uv pip install snowflake-ml-python xgboost \n\n✓  BASH  python session_helper.py  (connectivity check)\n  └─ ✅ Connected successfully!\n\n✓  WRITE  /Users/shtanaka/project/sfquickstarts/eda_ltv.py +330\n\n✓  BASH  python eda_ltv.py\n  └─ ... (99 more lines)\n\n✓  READ  eda_output/09_feature_importance_rf.png\n✓  READ  eda_output/04_correlation_with_target.png\n✓  READ  eda_output/01_target_distribution.png\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEDA results summary:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E  EDA Results and Feature Recommendations\n\n  Dataset Overview\n  • 96,264 transactions, 500 customers, 18 months (2024-07-01 to 2025-12-30)\n  • Zero nulls across all 5 columns\n  • Feature/label split: 80,491 rows for features (before Oct 1, 2025),\n    15,773 rows for the 90-day target window\n\n  Key EDA Findings\n\n  Target variable (90-day spend):\n  • Right-skewed: median $8,767 vs mean $12,595\n  • Range: $1,265 to $85,492\n\n  Correlation analysis reveals two dominant signal families:\n  1. Monetary features (r = 0.73–0.93): TOTAL_SPEND, SPEND_90D, STDDEV_TXN_AMOUNT, AVG_TXN_AMOUNT\n  2. Frequency features (r = 0.58–0.72): TXN_COUNT, TXN_COUNT_90D, TXN_COUNT_30D\n\n  Random Forest baseline: 5-fold CV R² = 0.646\n\n  Recommended Features — Tier 1 (Strongest Predictors)\n\n  ┌───────────────────┬─────────────┬───────────────┐\n  │ Feature           │ Correlation │ RF Importance │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ TOTAL_SPEND       │ 0.93        │ 0.741         │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ SPEND_90D         │ 0.88        │ 0.041         │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ STDDEV_TXN_AMOUNT │ 0.81        │ 0.054         │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ AVG_TXN_AMOUNT    │ 0.79        │ 0.021         │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ SPEND_30D         │ 0.78        │ 0.004         │\n  └───────────────────┴─────────────┴───────────────┘\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETrain the Model — CLI Output\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E> Build those features and train a regression model to predict each customer's\n  total spend in the next 90 days. Use two different algorithms, XGBoost and\n  Random Forest, and evaluate the best one. Use 20% of the data as the eval set.\n\n✓  READ  experiment-tracking/SKILL.md 456 lines\n\n✓  WRITE  /Users/shtanaka/project/sfquickstarts/train_ltv_model.py +358\n\n* Script details:\n  • Features: 28 features from EDA (RFM, spend windows, category/channel shares, trends)\n  • Split: 80% train / 20% eval (random_state=42)\n  • Model 1 — XGBoost: 500 trees, lr=0.05, early stopping at 30 rounds\n  • Model 2 — Random Forest: 500 trees, max_depth=15\n  • Experiment tracking: Both runs logged to LTV_90D_SPEND_PREDICTION\n\n✓  BASH  python train_ltv_model.py\n  └─ ... training complete\n\n✓  READ  eda_output/10_actual_vs_predicted.png\n✓  READ  eda_output/11_residual_distributions.png\n✓  READ  eda_output/12_feature_importance_comparison.png\n\n  Model Comparison (20% Eval Set — 100 customers)\n\n  ┌────────┬─────────┬───────────────┬──────────────────────────┐\n  │ Metric │ XGBoost │ Random Forest │ Winner                   │\n  ├────────┼─────────┼───────────────┼──────────────────────────┤\n  │ R²     │ 0.8088  │ 0.7872        │ XGBoost                  │\n  ├────────┼─────────┼───────────────┼──────────────────────────┤\n  │ RMSE   │ $3,782  │ $3,989        │ XGBoost                  │\n  ├────────┼─────────┼───────────────┼──────────────────────────┤\n  │ MAE    │ $2,791  │ $2,787        │ Random Forest (marginal) │\n  ├────────┼─────────┼───────────────┼──────────────────────────┤\n  │ MAPE   │ 37.86%  │ 32.86%        │ Random Forest            │\n  └────────┴─────────┴───────────────┴──────────────────────────┘\n\n  Best Model: XGBoost (by R²)\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAppendix A — Load Pre-Built Dataset from S3\u003C/h2\u003E\n","\u003Cp\u003EIf you prefer to load a pre-built dataset instead of generating synthetic data with Cortex Code, run the following SQL in a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight-worksheets-gs#create-worksheets-from-a-sql-file\"\u003ESnowsight SQL Worksheet\u003C/a\u003E or paste it as a prompt in Cortex Code CLI. You can also download \u003Ca href=\"https://github.com/Snowflake-Labs/cortex-code-samples/blob/main/data-science-ml/setup.sql\"\u003Esetup.sql\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE DATABASE IF NOT EXISTS COCO_DB;\nCREATE SCHEMA IF NOT EXISTS COCO_DB.COCO_SCHEMA;\nCREATE WAREHOUSE IF NOT EXISTS COCO_WH\n  WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND = 60\n  AUTO_RESUME = TRUE;\n\nUSE DATABASE COCO_DB;\nUSE SCHEMA COCO_SCHEMA;\nUSE WAREHOUSE COCO_WH;\n\nCREATE OR REPLACE FILE FORMAT ml_csvformat\n  SKIP_HEADER = 1\n  FIELD_OPTIONALLY_ENCLOSED_BY = '"'\n  TYPE = 'CSV';\n\nCREATE OR REPLACE STAGE ml_ltv_data_stage\n  FILE_FORMAT = ml_csvformat\n  URL = 's3://sfquickstarts/sfguide_getting_started_with_cortex_code_for_ds_ml/ltv_transactions/';\n\nCREATE OR REPLACE TABLE ML_LTV_TRANSACTIONS (\n\tCUSTOMER_ID VARCHAR(16777216),\n\tTRANSACTION_TIME TIMESTAMP_NTZ(9),\n\tAMOUNT NUMBER(12,2),\n\tPRODUCT_CATEGORY VARCHAR(15),\n\tCHANNEL VARCHAR(8)\n);\n\nCOPY INTO ML_LTV_TRANSACTIONS\n  FROM @ml_ltv_data_stage;\n\nSELECT 'Setup complete — ML_LTV_TRANSACTIONS loaded.' AS STATUS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter running the SQL, return to the \u003Ca href=\"#explore-the-data-eda\"\u003EExplore the Data\u003C/a\u003E step.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAppendix B — Real-Time Inference on SPCS\u003C/h2\u003E\n","\u003Cp\u003EAs an alternative to batch inference on a Snowflake Warehouse, you can deploy the model as a REST endpoint on Snowpark Container Services (SPCS) for real-time inference.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrerequisite:\u003C/strong\u003E A compute pool configured for Snowpark Container Services.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EPrompt\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003ELog the better model with metrics into Snowflake Model Registry, and use SPCS to create a REST endpoint for online inference.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex Code handles the \u003Ccode\u003Elog_model()\u003C/code\u003E call with the target platform set to \u003Ccode\u003ESNOWPARK_CONTAINER_SERVICES\u003C/code\u003E, then creates the SPCS service and endpoint for real-time inference.\u003C/p\u003E\n","\u003Cp\u003EThen test the endpoint with latency profiling:\u003C/p\u003E\n","\u003Ch3\u003EPrompt\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003ECreate feature profiles for 50 customers and run LTV predictions using the REST API for online inference running on SPCS. Show the top 10 highest predicted LTV customers and a latency profile (p50, p95, p99).\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex Code sends HTTP requests to the SPCS REST endpoint and displays results including:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EPredicted 90-day spend for each customer profile\u003C/li\u003E\u003Cli\u003EPer-request latency measurements\u003C/li\u003E\u003Cli\u003EA latency profile summary (p50, p95, p99) to help you understand the real-time performance characteristics of your deployment\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Build Your First ML Model in Snowflake with Agentic ML",":type":"snowflake-site/components/contentfragment",":itemsOrder":[],":items":{},"elements":{"quickstartArticleBody":{"title":"Quickstart Article Body","dataType":"string","value":"\u003C!-- ------------------------ --\u003E\n\u003E 🎥 **Prefer a guided walkthrough?** Follow along in the [virtual hands-on lab](https://www.snowflake.com/en/webinars/virtual-hands-on-lab/build-your-first-agentic-ml-pipeline-with-natural-language-2026-05-28/).\n\n## 1. Overview\n\n[Snowflake ML](http://www.snowflake.com/ml) is changing how teams work with agentic ML, an autonomous, reasoning-based system that enables developers to use agents to plan and execute tasks across the entire ML pipeline. In this quickstart, learn how to build and run a customer lifetime value (LTV) prediction model with only a handful of prompts so that you can go from raw data to production predictions in minutes, not weeks, with [Cortex Code](https://www.snowflake.com/en/product/features/cortex-code/), Snowflake's AI native coding agent. Cortex Code is available both as a CLI and directly in Snowsight, Snowflake's web interface.\n\n\u003E **Important:** Cortex Code is powered by LLMs and is non-deterministic. The code it generates may differ from what is shown in this guide. Always review the output and verify that the results match your expectations before proceeding to the next step.\n\n### What You'll Learn\n- Generate realistic synthetic e-commerce data with natural language prompts\n- Perform exploratory data analysis and feature engineering conversationally\n- Train and compare multiple regression models inside Snowflake\n- Log models with metrics to the Snowflake Model Registry\n- Run batch inference on a Snowflake Warehouse\n- (Optional) Deploy a model as a REST API on Snowpark Container Services (SPCS) for real-time inference\n\n### What You'll Build\nA complete customer LTV prediction pipeline featuring:\n- Synthetic e-commerce transactions dataset (~500 customers, ~100,000 transactions over 18 months)\n- Trained regression model predicting a customer's total spend in the next 90 days\n- Registered model in the Snowflake Model Registry with evaluation metrics\n- Batch inference predictions via Snowflake Warehouse\n- (Optional) A real-time inference REST endpoint on SPCS with latency profiling\n\n### Prerequisites\n- Sign up for the 30-day [free trial](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) of Snowflake. Have `ACCOUNTADMIN` role or a role with permissions to create databases, schemas, tables, and models\n- [Cortex Code in Snowsight](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-snowsight) (no local installation required)\n- A dedicated Snowflake warehouse\n- (Optional for SPCS) A compute pool configured for Snowpark Container Services — see the [official setup guide](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/common-setup)\n- Familiarity with basic ML concepts (training, evaluation, inference)\n\n\u003E **Using Cortex Code CLI?** The same prompts work in both interfaces. See [Cortex Code CLI Walkthrough](#cortex-code-cli-walkthrough) for CLI-specific setup and terminal output examples.\n\n\u003C!-- ------------------------ --\u003E\n## 2. Setup\n\n### Cortex Code in Snowsight\n\n[Cortex Code](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code) is an AI agent built into Snowflake, designed for data engineering, analytics, ML, and agent-building tasks. It operates autonomously within your Snowflake environment, leveraging deep knowledge of RBAC, schemas, and platform best practices.\n\n1. Open a Workspace Notebook by going to the sidebar and clicking on Projects \u003E Workspaces; then in the \"My Workspace\" panel, click on \"+ Add new\" \u003E Notebook.\n\n2. Once the notebook loads, look for Cortex Code in the lower-right corner of Snowsight.\n\n\u003E Note: Cortex Code is environment aware so using it in a Workspace Notebook will give the best results as it will have access to all the tools provided by the notebook. When relevant, generated code will be inserted into the notebook and run on your behalf.\n\n\nYou are now ready to start prompting Cortex Code to build your ML pipeline.\n\n\u003C!-- ------------------------ --\u003E\n## 3. Generate Synthetic Data\n\nFirst, let's create the database objects and generate synthetic e-commerce transaction data using Cortex Code.\n\n### Prompt\n\n```\nGenerate realistic looking synthetic data in database COCO_DB and schema COCO_SCHEMA \n(create if it doesn't exist). Create a table ML_LTV_TRANSACTIONS\nwith ~100000 transactions from ~500 customers over an 18-month period. Include\nCUSTOMER_ID, TRANSACTION_TIME, AMOUNT, PRODUCT_CATEGORY, and CHANNEL. Make the\ndata realistic: customers should have varying purchase frequencies (some buy\nweekly, others monthly), amounts should vary by category (electronics $50-$2000,\ngroceries $10-$200, apparel $20-$500), and channels should be web, mobile, or\nin-store. About 10% of customers should be high-value (frequent buyers with\nhigher average spend).\n```\n\n### What Gets Generated\n\nEnter the prompt in the Cortex Code chat panel. Cortex Code analyzes the request and breaks it into a multi-step plan:\n\n![Generate synthetic data by Cortex Code in Snowsight](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-code-in-snowsight-for-data-science-ml/generate-synthetic-data-by-cortex-code.jpg?v=575b6e9d)\n\nCortex Code generates the SQL or Python code to create the database objects and populate the table, then executes it automatically. You will see the code and results appear in a new Notebook cell:\n\n![Basic statistics by Cortex Code in Snowsight](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-code-in-snowsight-for-data-science-ml/show-basic-statistics-by-cortex-code.jpg?v=575b6e9d)\n\n\u003E Note: Due to the inherent randomness in how LLMs generate text, your results may vary slightly from what is shown in this tutorial.\n\nTo verify the data was generated correctly, run the following SQL in a Snowsight worksheet:\n\n\u003E Note: `COCO_DB.COCO_SCHEMA` is an example database and schema. If Cortex Code saved the data to a different database or schema in your environment, update these values before running the query.\n\n```sql\nSELECT * FROM COCO_DB.COCO_SCHEMA.ML_LTV_TRANSACTIONS LIMIT 10;\n```\n\nYou should see 10 rows with columns like `CUSTOMER_ID`, `TRANSACTION_TIME`, `AMOUNT`, `PRODUCT_CATEGORY`, and `CHANNEL`. \n\n\u003E **Alternative:** If you prefer to load a pre-built dataset instead of generating data, see [Appendix A — Load Pre-Built Dataset from S3](#appendix-a-load-pre-built-dataset-from-s3) at the end of this guide.\n\n\u003C!-- ------------------------ --\u003E\n## 4. Explore the Data (EDA)\n\nBefore training a model, analyze patterns to identify the right features for predicting customer lifetime value.\n\n### Prompt\n\n```\nDo exploratory data analysis and recommend the features needed to train a regression model that can predict each customer's total spend in the next 90 days.\n```\n\n### What Gets Generated\n\nCortex Code first verifies the table and shows a summary (row count, customer count, date range, and category breakdown), then performs deeper analysis across multiple steps — purchase frequency, spending distributions, recency patterns, and category preferences — and summarizes key findings with recommended features.\n\nIf the table is empty (or missing), see [Appendix A](#appendix-a-load-pre-built-dataset-from-s3) to load the pre-built dataset and retry.\n\n![EDA results and feature recommendations in Cortex Code](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-code-in-snowsight-for-data-science-ml/snowsight-cortex-code-eda-feature-recommendations.jpg?v=575b6e9d)\n\nIn this example, Cortex Code identifies signals such as purchase frequency trends, average order value by customer segment, recency of last purchase, and preferred product categories. These insights translate into features like total_transactions, avg_amount, days_since_last_purchase, favorite_category, and channel_distribution.\n\nThe EDA step typically reveals patterns such as:\n- High-value customers purchase more frequently and have higher average order values\n- Recency of last purchase is a strong predictor of future spend\n- Certain product categories correlate with higher lifetime value\n- Channel preferences (web vs. mobile vs. in-store) vary across customer segments\n\n\u003C!-- ------------------------ --\u003E\n## 5. Train the Model\n\nWith our features identified, we can now train a regression model. XGBoost and Random Forest are excellent choices for this kind of tabular prediction task.\n\n### Prompt\n\n```\nBuild those features and train a regression model to predict each customer's total spend in the next 90 days. Use two different algorithms, XGBoost and Random Forest, and evaluate the best one. Use 20% of the data as the eval set.\n```\n\n### What Gets Generated\n\nCortex Code typically creates a Notebook, generates feature engineering steps, trains two models, and reports evaluation metrics so you can choose the best performer.\n\n![Training and evaluation workflow created by Cortex Code](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-code-in-snowsight-for-data-science-ml/snowsight-cortex-code-train-two-models.jpg?v=575b6e9d)\n\nIn this example, Cortex Code generates Python for feature engineering (aggregating per-customer metrics from the transaction history), runs training/evaluation steps, and produces a comparison section (metrics like RMSE, MAE, and R-squared) to help you pick the best model.\n\nCortex Code will:\n1. Engineer the features based on the EDA recommendations (per-customer aggregations over the training window)\n2. Split the data into training (80%) and evaluation (20%) sets\n3. Train two different regression algorithms (e.g., XGBoost and Random Forest)\n4. Compare their performance using metrics such as RMSE, MAE, and R-squared\n5. Recommend the better-performing model\n\nReview the evaluation metrics to confirm the model meets your requirements before proceeding.\n\n\u003C!-- ------------------------ --\u003E\n## 6. Log to Model Registry and Run Inference\n\nNow register the better model to the Snowflake Model Registry and run batch inference.\n\n### Prompt\n\n```\nLog the better model with metrics into Snowflake Model Registry, and use Snowflake Warehouse for inference.\n```\n\nCortex Code handles the `log_model()` call with appropriate parameters including model metrics, sample input for schema inference, and the target platform set to `WAREHOUSE`.\n\n![Model logged to Snowflake Model Registry with metrics](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-code-in-snowsight-for-data-science-ml/snowsight-cortex-code-model-registry-log-model.jpg?v=575b6e9d)\n\nThen generate predictions:\n\n### Prompt\n\n```\nCreate feature profiles for 50 customers and run LTV predictions for them. Show the top 10 highest predicted LTV customers.\n```\n\nCortex Code generates the customer feature profiles, runs inference via your Snowflake Warehouse, and displays the predicted 90-day spend for each customer (sorted by highest predicted LTV).\n\n![Batch inference results for LTV predictions](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-code-in-snowsight-for-data-science-ml/snowsight-cortex-code-batch-inference-100-requests.jpg?v=575b6e9d)\n\n\u003E **Optional:** To deploy the model as a real-time REST endpoint on SPCS instead, see Appendix B — Real-Time Inference on SPCS at the end of this guide.\n\n\u003C!-- ------------------------ --\u003E\n## 7. Debug and Recover from Errors\n\nDuring any natural language coding session, errors are inevitable. The great thing about Cortex Code is its ability to self-correct by assessing the situation, environment, and error to fix issues automatically.\n\n### Common Scenarios\n\n**Model Registration Errors**\n\nWhen `log_model()` fails due to parameter issues (e.g., target platform mismatch), Cortex Code diagnoses the error and re-registers the model with corrected parameters automatically.\n\n**Notebook Execution Issues**\n\nWhen a cell fails due to missing imports or data type mismatches, Cortex Code detects the issue, adjusts the code, and re-executes the cell.\n\n**Feature Engineering Errors**\n\nIf a feature column is missing or a SQL view fails, Cortex Code investigates the schema, identifies the root cause, and regenerates the feature engineering step.\n\n### Best Practices\n\n1. Start with `ACCOUNTADMIN` for initial setup, then create dedicated roles\n2. Monitor compute pool resources during SPCS deployment\n3. Review Cortex Code's explanations when it makes corrections\n4. Use the Snowsight Notebook environment for the best interactive experience with visualizations\n\n\n\u003C!-- ------------------------ --\u003E\n## 8. Conclusion And Resources\n\nCongratulations! You've successfully built a complete customer LTV prediction model using only a handful of natural language prompts in [Snowflake ML](http://www.snowflake.com/ml).\n\n### What You Built\n\n![LTV Prediction Pipeline Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-code-in-snowsight-for-data-science-ml/architecture-diagram.svg?v=575b6e9d)\n\n### What You Learned\n- Generate realistic synthetic e-commerce data with natural language prompts\n- Perform comprehensive exploratory data analysis with automated feature recommendations\n- Train and compare multiple regression models for LTV prediction\n- Log models with metrics to the Snowflake Model Registry\n- Run batch inference on a Snowflake Warehouse\n\n### Related Resources\n\nWeb pages:\n- [Snowflake ML](http://www.snowflake.com/ml) - Integrated set of capabilities for development, MLOps and inference leading with agentic ML\n- [Snowflake Notebooks](https://www.snowflake.com/en/product/features/notebooks/) - Jupyter-based notebooks in Snowflake Workspaces\n- [Cortex Code](https://www.snowflake.com/en/product/features/cortex-code/) - Snowflake's AI native coding agent that boosts ML productivity\n\nTechnical Documentation:\n- [Snowflake ML Documentation](https://docs.snowflake.com/en/developer-guide/snowflake-ml/overview) - Official Snowflake ML developer guide\n- [Snowflake ML Quickstart](https://docs.snowflake.com/en/developer-guide/snowflake-ml/quickstart) - Hands-on guides to get started with Snowflake ML\n- [Cortex Code Documentation](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code) - Getting started with Cortex Code\n- [Cortex Code in Snowsight](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-snowsight) - Browser-based experience\n- [Cortex Code CLI](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli) - Command-line experience\n- [Snowflake Model Registry](https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview) - Register, version, and deploy ML models\n- [Snowpark Container Services](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview) - Deploy and manage containerized workloads\n\n\u003C!-- ------------------------ --\u003E\n## Optional A - Cleanup\n\nTo avoid ongoing Snowflake credit consumption, you can clean up the resources created in this guide. There are two approaches: a **Cortex Code prompt** and **Manual SQL**.\n\nIf you completed this quickstart in a single session and your environment does not contain other data, use \"A-1 Cortex Code\" prompt for a quick cleanup.\n\nIf you worked through this quickstart over multiple days or your environment contains resources unrelated to this guide, use \"A-2 Manual SQL\" approach to ensure you only drop the intended objects.\n\n### A-1. Cortex Code\n\n\u003E Note: This prompt works best within the same Cortex Code session where the database and other objects like model were created. If you are cleaning up resources from a previous session (e.g., a different day) or your environment contains objects unrelated to this quickstart, use the Manual SQL approach below for more precise control below.\n\n\n```\nDrop Database and model that we created earlier in this session\n```\n\nCortex Code will generate and execute the appropriate DROP statements for each resource.\n\n### A-2. Manual SQL\n\nIf you prefer to run the cleanup manually:\n\n\u003E Note: `COCO_DB.COCO_SCHEMA` is an example database and schema, and `COCO_WH` is an example warehouse name. If Cortex Code saved the data to a different database or created a different warehouse in your environment, update these values before running the query.\n\n\n```sql\n-- Drop the database and all objects within it (table, schema, stage, etc.)\nDROP DATABASE IF EXISTS COCO_DB;\n\n-- Drop the model from the Model Registry\nDROP MODEL IF EXISTS COCO_DB.COCO_SCHEMA.ML_LTV_PREDICTOR;\n\n-- Drop the warehouse\nDROP WAREHOUSE IF EXISTS COCO_WH;\n```\n\n\u003E Note: `DROP DATABASE` removes all schemas, tables, and stages inside it. Make sure you no longer need any of the data before running this command.\n\n\n\u003C!-- ------------------------ --\u003E\n## Optional B - Cortex Code CLI Walkthrough\n\nThe same prompts used throughout this guide work identically in Cortex Code CLI. This section shows CLI-specific setup and sample terminal output so you can compare what to expect in a terminal session.\n\n### Setup\n\nInstall the CLI:\n\n```bash\ncurl -LsS https://ai.snowflake.com/static/cc-scripts/install.sh | sh\n```\n\nAfter installing, run `cortex` and follow the setup wizard to connect to your Snowflake account. For detailed instructions, refer to the [Cortex Code CLI documentation](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli).\n\nVerify your connection:\n\n```\nWhat role am I using and what databases can I access?\n```\n\n\u003E **Tip**: Run Cortex Code CLI inside a VS Code or Cursor terminal to view generated files side-by-side.\n\n\u003E **Tip:** Prefix table names with `#` (e.g., `#COCO_DB.COCO_SCHEMA.ML_LTV_TRANSACTIONS`) to ground the conversation to a specific object.\n\n### Generate Synthetic Data — CLI Output\n\nAfter entering the Generate Synthetic Data prompt, Cortex Code CLI shows a summary like this:\n\n```\n  Summary Statistics\n\n  ┌───────────┬──────────┐\n  │ Metric    │ Value    │\n  ├───────────┼──────────┤\n  │ Customers │ 500      │\n  ├───────────┼──────────┤\n  │ Min LTV   │ $12,930  │\n  ├───────────┼──────────┤\n  │ P25       │ $36,745  │\n  ├───────────┼──────────┤\n  │ Median    │ $54,617  │\n  ├───────────┼──────────┤\n  │ Mean      │ $76,769  │\n  ├───────────┼──────────┤\n  │ P75       │ $86,724  │\n  ├───────────┼──────────┤\n  │ Max       │ $495,857 │\n  ├───────────┼──────────┤\n  │ Std Dev   │ $67,972  │\n  └───────────┴──────────┘\n\n  Distribution (Histogram)\n\n    LTV Range           Customers\n    $12,930 - $36,868   126  █████████████████████████████████████\n    $37,086 - $61,126   170  ██████████████████████████████████████████████████\n    $61,382 - $84,439    74  ██████████████████████\n    $85,925 - $109,095   35  ██████████\n    $110,606 - $133,179  26  ████████\n    $134,850 - $157,594  18  █████\n    $158,039 - $181,760  19  ██████\n    $182,181 - $206,059   7  ██\n    $209,265 - $241,987  10  ███\n    $257,600 - $283,419   5  █\n    $345,815 - $387,939   7  ██\n    $410,296 - $495,857   3  █\n```\n\n### Explore the Data (EDA) — CLI Output\n\nCortex Code CLI reads its built-in ML skill, detects the local Python environment, and runs the EDA in incremental steps:\n\n```\n\u003E Do exploratory data analysis and recommend the features needed to train a regression model that can predict each customer's total spend in the next 90 days.\n\n✓  SKILL  machine-learning\n\n✓  READ  cli-environment.md 194 lines\n\n✓  READ  ml-development/SKILL.md 279 lines\n\n* Let me set up the environment and build a comprehensive EDA script.\n\n✓  BASH  which uv\n  └─ /Users/shtanaka/.local/bin/uv\n\n✓  BASH  cortex env detect\n  └─ {\"directory\": \"/Users/shtanaka/project/sfquickstarts\", ...}\n\n✓  BASH  uv venv /Users/shtanaka/project/sfquickstarts/.venv --python 3.10 2\u003E&1\n  └─ Creating virtual environment at: .venv\n\n✓  BASH  uv pip install snowflake-ml-python xgboost \n\n✓  BASH  python session_helper.py  (connectivity check)\n  └─ ✅ Connected successfully!\n\n✓  WRITE  /Users/shtanaka/project/sfquickstarts/eda_ltv.py +330\n\n✓  BASH  python eda_ltv.py\n  └─ ... (99 more lines)\n\n✓  READ  eda_output/09_feature_importance_rf.png\n✓  READ  eda_output/04_correlation_with_target.png\n✓  READ  eda_output/01_target_distribution.png\n```\n\nEDA results summary:\n\n```\n  EDA Results and Feature Recommendations\n\n  Dataset Overview\n  • 96,264 transactions, 500 customers, 18 months (2024-07-01 to 2025-12-30)\n  • Zero nulls across all 5 columns\n  • Feature/label split: 80,491 rows for features (before Oct 1, 2025),\n    15,773 rows for the 90-day target window\n\n  Key EDA Findings\n\n  Target variable (90-day spend):\n  • Right-skewed: median $8,767 vs mean $12,595\n  • Range: $1,265 to $85,492\n\n  Correlation analysis reveals two dominant signal families:\n  1. Monetary features (r = 0.73–0.93): TOTAL_SPEND, SPEND_90D, STDDEV_TXN_AMOUNT, AVG_TXN_AMOUNT\n  2. Frequency features (r = 0.58–0.72): TXN_COUNT, TXN_COUNT_90D, TXN_COUNT_30D\n\n  Random Forest baseline: 5-fold CV R² = 0.646\n\n  Recommended Features — Tier 1 (Strongest Predictors)\n\n  ┌───────────────────┬─────────────┬───────────────┐\n  │ Feature           │ Correlation │ RF Importance │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ TOTAL_SPEND       │ 0.93        │ 0.741         │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ SPEND_90D         │ 0.88        │ 0.041         │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ STDDEV_TXN_AMOUNT │ 0.81        │ 0.054         │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ AVG_TXN_AMOUNT    │ 0.79        │ 0.021         │\n  ├───────────────────┼─────────────┼───────────────┤\n  │ SPEND_30D         │ 0.78        │ 0.004         │\n  └───────────────────┴─────────────┴───────────────┘\n```\n\n### Train the Model — CLI Output\n\n```\n\u003E Build those features and train a regression model to predict each customer's\n  total spend in the next 90 days. Use two different algorithms, XGBoost and\n  Random Forest, and evaluate the best one. Use 20% of the data as the eval set.\n\n✓  READ  experiment-tracking/SKILL.md 456 lines\n\n✓  WRITE  /Users/shtanaka/project/sfquickstarts/train_ltv_model.py +358\n\n* Script details:\n  • Features: 28 features from EDA (RFM, spend windows, category/channel shares, trends)\n  • Split: 80% train / 20% eval (random_state=42)\n  • Model 1 — XGBoost: 500 trees, lr=0.05, early stopping at 30 rounds\n  • Model 2 — Random Forest: 500 trees, max_depth=15\n  • Experiment tracking: Both runs logged to LTV_90D_SPEND_PREDICTION\n\n✓  BASH  python train_ltv_model.py\n  └─ ... training complete\n\n✓  READ  eda_output/10_actual_vs_predicted.png\n✓  READ  eda_output/11_residual_distributions.png\n✓  READ  eda_output/12_feature_importance_comparison.png\n\n  Model Comparison (20% Eval Set — 100 customers)\n\n  ┌────────┬─────────┬───────────────┬──────────────────────────┐\n  │ Metric │ XGBoost │ Random Forest │ Winner                   │\n  ├────────┼─────────┼───────────────┼──────────────────────────┤\n  │ R²     │ 0.8088  │ 0.7872        │ XGBoost                  │\n  ├────────┼─────────┼───────────────┼──────────────────────────┤\n  │ RMSE   │ $3,782  │ $3,989        │ XGBoost                  │\n  ├────────┼─────────┼───────────────┼──────────────────────────┤\n  │ MAE    │ $2,791  │ $2,787        │ Random Forest (marginal) │\n  ├────────┼─────────┼───────────────┼──────────────────────────┤\n  │ MAPE   │ 37.86%  │ 32.86%        │ Random Forest            │\n  └────────┴─────────┴───────────────┴──────────────────────────┘\n\n  Best Model: XGBoost (by R²)\n```\n\n\u003C!-- ------------------------ --\u003E\n## Appendix A — Load Pre-Built Dataset from S3\n\nIf you prefer to load a pre-built dataset instead of generating synthetic data with Cortex Code, run the following SQL in a [Snowsight SQL Worksheet](https://docs.snowflake.com/en/user-guide/ui-snowsight-worksheets-gs#create-worksheets-from-a-sql-file) or paste it as a prompt in Cortex Code CLI. You can also download [setup.sql](https://github.com/Snowflake-Labs/cortex-code-samples/blob/main/data-science-ml/setup.sql).\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nCREATE DATABASE IF NOT EXISTS COCO_DB;\nCREATE SCHEMA IF NOT EXISTS COCO_DB.COCO_SCHEMA;\nCREATE WAREHOUSE IF NOT EXISTS COCO_WH\n  WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND = 60\n  AUTO_RESUME = TRUE;\n\nUSE DATABASE COCO_DB;\nUSE SCHEMA COCO_SCHEMA;\nUSE WAREHOUSE COCO_WH;\n\nCREATE OR REPLACE FILE FORMAT ml_csvformat\n  SKIP_HEADER = 1\n  FIELD_OPTIONALLY_ENCLOSED_BY = '\"'\n  TYPE = 'CSV';\n\nCREATE OR REPLACE STAGE ml_ltv_data_stage\n  FILE_FORMAT = ml_csvformat\n  URL = 's3://sfquickstarts/sfguide_getting_started_with_cortex_code_for_ds_ml/ltv_transactions/';\n\nCREATE OR REPLACE TABLE ML_LTV_TRANSACTIONS (\n\tCUSTOMER_ID VARCHAR(16777216),\n\tTRANSACTION_TIME TIMESTAMP_NTZ(9),\n\tAMOUNT NUMBER(12,2),\n\tPRODUCT_CATEGORY VARCHAR(15),\n\tCHANNEL VARCHAR(8)\n);\n\nCOPY INTO ML_LTV_TRANSACTIONS\n  FROM @ml_ltv_data_stage;\n\nSELECT 'Setup complete — ML_LTV_TRANSACTIONS loaded.' AS STATUS;\n```\n\nAfter running the SQL, return to the [Explore the Data](#explore-the-data-eda) step.\n\n\u003C!-- ------------------------ --\u003E\n## Appendix B — Real-Time Inference on SPCS\n\nAs an alternative to batch inference on a Snowflake Warehouse, you can deploy the model as a REST endpoint on Snowpark Container Services (SPCS) for real-time inference.\n\n\u003E **Prerequisite:** A compute pool configured for Snowpark Container Services.\n\n### Prompt\n\n```\nLog the better model with metrics into Snowflake Model Registry, and use SPCS to create a REST endpoint for online inference.\n```\n\nCortex Code handles the `log_model()` call with the target platform set to `SNOWPARK_CONTAINER_SERVICES`, then creates the SPCS service and endpoint for real-time inference.\n\nThen test the endpoint with latency profiling:\n\n### Prompt\n\n```\nCreate feature profiles for 50 customers and run LTV predictions using the REST API for online inference running on SPCS. Show the top 10 highest predicted LTV customers and a latency profile (p50, p95, p99).\n```\n\nCortex Code sends HTTP requests to the SPCS REST endpoint and displays results including:\n- Predicted 90-day spend for each customer profile\n- Per-request latency measurements\n- A latency profile summary (p50, p95, p99) to help you understand the real-time performance characteristics of your deployment\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"title":"Quickstart Article Logo Image","dataType":"string",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-3730c61693","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-f2d23c70ba",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":itemsOrder":["quickstart_last_modi","text"],":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-29afbf12bf","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-06-09",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-bd9aaf66d2","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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Overview\u003C/h2\u003E","\u003Ch2\u003E2. Setup\u003C/h2\u003E","\u003Ch2\u003E3. Generate Synthetic Data\u003C/h2\u003E","\u003Ch2\u003E4. Explore the Data (EDA)\u003C/h2\u003E","\u003Ch2\u003E5. Train the Model\u003C/h2\u003E","\u003Ch2\u003E6. Log to Model Registry and Run Inference\u003C/h2\u003E","\u003Ch2\u003E7. Debug and Recover from Errors\u003C/h2\u003E","\u003Ch2\u003E8. 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