Machine Learning and Generative AI using Snowflake Cortex
Building Enterprise AI Solutions with Snowpark ML and Snowflake Cortex
Duration
5 Days
Level
Advanced Level
Design and Tailor this course
As per your team needs
Overview
This comprehensive 40-hour program provides a deep dive into building Machine Learning and Generative AI solutions directly within Snowflake using Snowpark ML and Snowflake Cortex. Participants will learn how to design ML pipelines, execute distributed model training, operationalize models using Snowflake MLOps, and leverage Cortex ML and LLM Functions for real-world enterprise use cases. The course emphasizes scalable architectures, cost optimization, governance, and integration patterns for enterprise deployment.
Audience
This program is designed for technical roles focused on the Snowflake Data Cloud:
- Data Scientists: Transitioning traditional Python/Sklearn workflows to Snowflake’s distributed environment.
- Data Engineers: Building robust pipelines for AI/ML and managing the feature engineering lifecycle.
- AI Engineers: Developing LLM-powered applications using integrated Cortex functions.
- Data Architects: Designing secure and governed AI architectures that minimize data movement.
- MLOps Engineers: Managing model registration, versioning, and deployment within Snowflake.
Prerequisites
- Technical Background: Working knowledge of SQL and Python.
- Platform Knowledge: Familiarity with Snowflake architecture (Warehouses, Roles, Stages).
- Statistics: Basic understanding of regression, classification, and evaluation metrics.
Curriculum
- Machine Learning Core Concepts
- Supervised vs. Unsupervised learning and evaluation metrics.
- Bias-variance tradeoff and overfitting/underfitting.
- Data Generation: Creating time-series and synthetic datasets for training.
- Snowpark Architecture
- The Snowpark execution model and pushdown compute.
- Differences between traditional ML workflows and Snowflake-native workflows.
- Data Loading and Exploration
- Ingestion via SQL, Python, and External Stages.
- Exploratory Data Analysis (EDA) using Snowsight and Snowflake Notebooks.
- Snowpark ML APIs
- Distributed preprocessing: Feature scaling and categorical encoding.
- Parallelized training using Snowpark ML vs. Scikit-Learn.
- Hyperparameter Optimization (HPO)
- Distributed HPO strategies and resource utilization.
- Metric computation and model comparison dashboards.
- Snowflake MLOps
- Experiment logging and the Snowflake Model Registry.
- Model versioning, batch inference, and real-time serving.
- Classification and Forecasting
- Binary and Multi-class classification for banking and risk use cases.
- Time-series forecasting: Sales and weather prediction models.
- Anomaly Detection and Boosting
- Statistical anomaly detection for fraud and IoT monitoring.
- Gradient Boosting intuition and practical applications.
- Interpretation and Governance
- Contribution Explorer: Explaining prediction shifts and feature impact.
- Role-Based Access Control (RBAC) and cost implications for ML Functions.
- Introduction to Cortex LLM Functions
- LLM architecture within Snowflake and token considerations.
- Built-in Functions: COMPLETE, EXTRACT_ANSWER, SENTIMENT, and SUMMARIZE.
- Enterprise LLM Applications
- Customer feedback analysis and contract clause extraction.
- Automated reporting and support ticket summarization.
- Snowflake Copilot and Document AI
- SQL generation reliability and guardrails.
- Parsing structured documents with Document AI.
- Integration Patterns
- LangChain + Snowflake integration.
- External LLM API connections and secure data access.
- Use Case 1 – Legal and Procurement (ClauseAI)
- Contract risk scoring and automated compliance checks.
- Use Case 2 – Text-to-SQL Assistant
- Natural language query generation with governance validation.
- Cost Management and Governance
- Token cost monitoring and LLM usage auditing.
Duration
5 Days
Level
Advanced Level
Design and Tailor this course
As per your team needs
