Snowflake Generative AI Professional Certificate course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview (80-120 words) describing structure and time commitment.
Module 1: Introduction to Generative AI & Snowflake
Estimated time: 10 hours
- Understand generative AI fundamentals and large language models (LLMs)
- Explore Snowflake Data Cloud architecture
- Learn about enterprise AI use cases in data platforms
- Get started with Snowflake Cortex and built-in AI capabilities
Module 2: Working with Data for AI Applications
Estimated time: 14 hours
- Prepare structured and semi-structured data in Snowflake
- Build and manage data pipelines for AI workloads
- Use SQL and Snowflake features for dataset preparation
- Apply best practices for scalable and secure data storage
Module 3: Building Generative AI Applications
Estimated time: 20 hours
- Implement embeddings and vector search in Snowflake
- Build retrieval-augmented generation (RAG) workflows
- Integrate LLM-powered features into applications
- Design AI-driven analytics solutions for business use cases
Module 4: Deployment, Governance & Security
Estimated time: 12 hours
- Ensure compliance, privacy, and secure data access
- Monitor AI workloads and performance in Snowflake
- Manage costs in generative AI deployments
- Apply responsible AI practices in enterprise environments
Module 5: Capstone Project
Estimated time: 16 hours
- Design an AI-powered data application using Snowflake tools
- Prepare, process, and analyze datasets for generative AI
- Implement embeddings and retrieval pipelines
- Present a scalable, secure enterprise AI solution
Prerequisites
- Familiarity with SQL fundamentals
- Basic understanding of cloud computing concepts
- Experience with data workflows is recommended
What You'll Be Able to Do After
- Build and deploy generative AI applications on Snowflake
- Implement retrieval-augmented generation (RAG) workflows
- Integrate LLMs into enterprise data pipelines
- Apply security and governance best practices to AI solutions
- Design scalable AI-driven analytics applications