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 Data Cloud
Estimated time: 6 hours
- Fundamentals of generative AI and large language models (LLMs)
- Architecture and components of the Snowflake Data Cloud
- Enterprise use cases for generative AI in analytics and automation
- Introduction to Snowflake Cortex and integrated AI capabilities
Module 2: Data Preparation & Management for AI
Estimated time: 8 hours
- Working with structured and semi-structured data in Snowflake
- Building scalable data pipelines using SQL and Snowflake tools
- Best practices for data governance, storage, and security
- Preparing datasets for embeddings and AI model interaction
Module 3: Building Generative AI Applications
Estimated time: 12 hours
- Creating embeddings using Snowflake Cortex
- Implementing vector search capabilities in Snowflake
- Building retrieval-augmented generation (RAG) pipelines
- Integrating LLM-powered features into data workflows
Module 4: AI Deployment, Monitoring & Governance
Estimated time: 6 hours
- Deploying generative AI solutions in Snowflake environments
- Monitoring AI model performance and system efficiency
- Managing security, access control, and compliance
- Implementing responsible AI practices in enterprise settings
Module 5: Final Project
Estimated time: 10 hours
- Design and implement an AI-powered data solution on Snowflake
- Prepare and process datasets using Snowflake tools
- Build and present a retrieval-based generative AI workflow
Prerequisites
- Familiarity with SQL and basic data concepts
- Basic understanding of cloud computing platforms
- Experience with data management or analytics is recommended
What You'll Be Able to Do After
- Design and build generative AI applications on the Snowflake Data Cloud
- Create and manage scalable data pipelines for AI workloads
- Implement vector search and embeddings for AI-driven solutions
- Build retrieval-augmented generation (RAG) pipelines in enterprise environments
- Deploy, monitor, and govern AI systems with security and compliance