Overview
Discover how to optimize Generative AI systems by combining Snorkel Flow with AWS Bedrock and SageMaker, focusing on RAG workflow improvements, data labeling techniques, and enterprise deployment strategies.
Syllabus
-
- Introduction to Generative AI Systems
-- Overview of Generative AI
-- Importance of optimization in AI systems
- Basics of AWS for AI
-- Introduction to AWS Bedrock
-- Introduction to AWS SageMaker
-- Overview of RAG (Retrieve-Augment-Generate) workflow in AWS
- Introduction to Snorkel Flow
-- Overview of data labeling challenges
-- Introduction to Snorkel Flow concepts
-- Advantages of using Snorkel for labeling
- Combining Snorkel Flow with AWS for RAG Optimization
-- Integration of Snorkel Flow with AWS Bedrock
-- Data labeling improvements for RAG workflows
-- Automating labeling with Snorkel in AWS environments
- Advanced Techniques in RAG Workflow Optimization
-- Techniques for efficient data retrieval
-- Optimizing data augmentation processes
-- Generating high-quality outputs
- Data Labeling Techniques for Generative AI
-- Weak supervision strategies
-- Labeling complex datasets with Snorkel
-- Quality assurance in labeling
- Enterprise Deployment Strategies
-- Scalability considerations on AWS
-- Security and compliance in AI deployment
-- Monitoring and maintaining AI systems in production
- Case Studies and Practical Applications
-- Real-world examples of optimized RAG workflows
-- Success stories of enterprise deployment with AWS and Snorkel
- Conclusion and Future Trends in Generative AI Optimization
-- Emerging technologies in AI system optimization
-- Future potential of AWS and Snorkel in AI development
Taught by
Tags
On-Demand
