Azure Managed Redis: Designed to Supercharge Your GenAI Apps

Overview

Master semantic caching and Azure Managed Redis to optimize AI applications, reduce LLM costs, and enhance performance through vector database implementation and real-world optimization techniques.

Syllabus

• Introduction to Azure Managed Redis
Overview of Redis and its integration with Azure
Benefits of using Azure Managed Redis for AI applications
• Understanding Semantic Caching
Concepts of semantic caching
Role of semantic caching in AI and machine learning models
Implementation strategies for semantic caching with Redis
• Optimizing AI Applications with Azure Managed Redis
Techniques to reduce latency in AI models
Leveraging Redis as an in-memory data store for AI applications
Case studies on performance improvements
• Reducing LLM Costs using Azure Managed Redis
Methods to minimize API calls and optimize data retrieval
Strategies for cost-effective AI application scaling
• Introduction to Vector Databases
Basics of vector databases and their importance in AI
Integration of Redis with vector databases
Example use cases for vector databases in GenAI applications
• Real-World Optimization Techniques
Best practices for optimizing AI models with managed Redis
Monitoring and maintaining Redis performance
Troubleshooting common issues and solutions
• Lab Sessions and Hands-on Workshops
Setting up Azure Managed Redis
Implementing semantic caching in a sample GenAI app
Creating and using vector databases with Redis
• Conclusion and Real-World Use Cases
Summary of key insights and strategies
Exploration of advanced scenarios and future trends in AI with Redis
• Final Project
Design an optimized GenAI application using Azure Managed Redis and vector databases
Present findings and optimizations achieved through course learnings

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Subjects

Programming