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
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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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- AI Developers and Engineers
- Data Scientists
- Machine Learning Engineers
- Cloud Solution Architects
- IT Professionals interested in AI and cloud technologies
- Students and researchers in the field of AI and machine learning
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- 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
- 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
- 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 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
- 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
- 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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- Basic understanding of machine learning and natural language processing (NLP)
- Familiarity with Python programming
- Experience with cloud computing concepts, preferably Azure
