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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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  • 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

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