• This course is designed for Software Developers, Data Scientists, Software Architects, Quality Assurance Engineers, Data Analysts to build and implement generative AI models. Familiarity with machine learning concepts (like neural networks) is helpful but not required.

• Familiarity with programming language – especially Python
• Basics of using Snowflake and SQL
• Prior knowledge of Machine Learning will be useful but not required

Course Outline:

1. Snowflake Overview
   1. Quick review of Snowflake Architecture
   2. Understanding data ingestion from external sources
   3. Demos and Labs
2. Machine Learning Overview
   1. Understand the concept of machine learning
   2. Learn about data wrangling & preparing data for machine learning
   3. Understanding machine learning techniques: Classification & Regression
   4. Learn about metrics for validating these techniques
   5. Working with hyper parameters, cross validation
   6. Build machine learning pipelines
   7. Multiple Demos and Labs
3. Machine Learning pipelines with Snowpark
   1. What are Snowpark components?
   2. Snowflake Python connector vs Snowpark – what is the difference?
   3. Working with UDF, Vectorized UDF, Functions, Procedures
   4. Pandas vs Snowpark Dataframes
   5. ML Pipelines with Snowpark
   6. Multiple Demos and Labs
4. Machine Learning pipelines with Snowpark ML (Cortex)
   1. Introduction to Snowpark ML APIs
   2. Data Collections with Filesystem and FileSet
   3. Distributed pipelines with Snowpark ML
   4. Hyper parameter tuning with Snowpark ML
   5. Model predictions with registered models
   6. Multiple Demos and Labs
5. Machine Learning Functions with Cortex
   1. What are machine learning functions in cortex
   2. Performing Time-series forecasting, anomaly detection, classification with ML Functions
   3. Using Snowflake SQL classes and instances
   4. Building Classification and Regression Models with Spark machine learning library
   5. Understanding the costs involved in using Machine Learning Functions
   6. Multiple Demos and Labs
6. LLM (Large Language Model) Functions with Cortex
   1. Understand support for various LLM models on Snowflake
   2. Integrate with ChatGPT models
   3. Using LLM functions: COMPLETE, SENTIMENT, SUMMARIZE, TRANSLATE, etc
   4. Understanding the costs involved in using these LLMs
   5. Multiple Demos and Labs

Training material provided: Yes (Digital format)

Hands-on Lab: Students will create a trial Snowflake account for the hands-on labs. If required virtual machines will be provided