Lilly Tech Systems
Snowflake AI & ML
Master AI and machine learning on the Snowflake Data Cloud. From Snowpark development and built-in ML functions to Cortex AI and Streamlit apps, learn to build intelligent applications without moving your data.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is Snowflake AI? Understand the Data Cloud, architecture, and how AI capabilities integrate with your data platform.
2. Snowpark
Build data pipelines and ML models using Python, Java, or Scala with Snowpark's DataFrame API running natively in Snowflake.
3. ML Functions
Use Snowflake's built-in ML functions for forecasting, anomaly detection, classification, and contribution explorer.
4. Cortex AI
Access LLMs, build RAG applications, and use AI functions like COMPLETE, SUMMARIZE, and TRANSLATE directly in SQL.
5. Streamlit in Snowflake
Build and deploy interactive data applications directly in Snowflake with Streamlit — no infrastructure management needed.
6. Best Practices
Performance optimization, cost management, security patterns, and production architecture for Snowflake AI workloads.
What You'll Learn
By the end of this course, you'll be able to:
Snowpark Development
Write Python data pipelines and ML models that execute natively inside Snowflake's compute engine.
Built-in ML
Use Snowflake's SQL-based ML functions for forecasting, anomaly detection, and classification without external tools.
Generative AI
Build LLM-powered applications with Cortex AI functions and retrieval-augmented generation.
Data Apps
Deploy interactive Streamlit applications inside Snowflake for data exploration and ML model interaction.