AI Development with RAG and LangChain on Azure

Unlocking Advanced AI Solutions with Retrieval-Augmented Generation and LangChain on Azure

Duration

2 days (8 hours per day)

Level

Intermediate Level

Design and Tailor this course

As per your team needs

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This course is designed to provide an in-depth understanding of how to leverage Retrieval-Augmented Generation (RAG) and LangChain with Azure to develop advanced AI solutions. The course focuses on practical, hands-on experience, enabling participants to build and deploy robust AI models using Azure’s powerful infrastructure. Through a combination of labs, demos, and real-world use cases, participants will gain the skills necessary to implement cutting-edge AI technologies in their organizations.

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Introduction to RAG and LangChain

Overview of Retrieval-Augmented Generation (RAG)
- Definition and significance in AI
- Use cases and applications
Introduction to LangChain
- Core concepts and functionalities
- Integrating LangChain with other AI tools
Setting Up the Environment
- Azure setup and configurations

Understanding Azure Code Stack for AI Development

Overview of Azure AI Services
- Cognitive Services, Machine Learning, and Bot Services
- Azure AI infrastructure and capabilities
Azure Code Stack Components
- Azure Databricks
- Azure Machine Learning Service
- Azure Synapse Analytics
Integration Techniques
- Connecting Azure services with LangChain
- Utilizing Azure resources in AI workflows

Practical Implementation of RAG with Azure

Building RAG Models
- Step-by-step guide to creating RAG models
- Configuring and optimizing model parameters
Deploying RAG Models on Azure
- Deployment strategies and best practices
- Monitoring and maintaining models in production
Hands-on Lab: Developing a RAG-based AI Application
- Real-world scenario implementation
- End-to-end application development and deployment

Advanced LangChain Techniques

Advanced Features of LangChain
- Custom chain creation
- Enhancing model performance with LangChain
Use Cases and Industry Applications
- Case studies of LangChain in various industries
- Building industry-specific AI solutions
Hands-on Lab: Customizing LangChain for Specific Use Cases
- Implementing advanced LangChain features
- Developing tailored solutions for different industries

Integrating RAG and LangChain with Azure Code Stack

Combining RAG and LangChain
- Strategies for seamless integration
- Leveraging Azure’s capabilities to enhance performance
End-to-End AI Solution Development
- Workflow for integrating RAG and LangChain in Azure
- Best practices and optimization techniques
Hands-on Lab: Comprehensive AI Solution Development
- Full cycle of AI solution development
- Real-world application and deployment

Hands-On Labs and Demos

Guided lab sessions for each module
Real-time problem solving and debugging
Interactive Demos
- Demonstrations of advanced AI capabilities
- Showcasing real-world applications and case studies

Conclusion and Future Directions

Course Recap
- Summary of key learnings and takeaways
Future Trends in AI and Azure
- Emerging technologies and their impact
- Continuous learning and development paths

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AI Development with RAG and LangChain on Azure

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