Developing Advanced LLM Applications


Course Number: AI-136WA
Duration: 4 days (26 hours)
Format: Live, hands-on

Advanced LLM Training Overview

This advanced Generative AI training is designed for developers who want to explore enterprise-grade Large Language Model (LLM) architectures and design patterns. This course covers chatbot architectures, Agentic RAG, LLM-powered agents, and model serving and deployment techniques. Participants learn how to design and implement advanced LLM-based applications using cutting-edge technologies and frameworks.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

In addition, some courses are available as live, instructor-led training from one of our partners.

Objectives

  • Design and implement advanced chatbot architectures with LLMs for personalized, context-aware interactions
  • Integrate chatbots seamlessly into enterprise systems  for streamlined workflows
  • Master agentic RAG architectures and techniques to build powerful systems capable of multi-hop reasoning and graph-based knowledge representation
  • Build and orchestrate LLM-powered agents for autonomous decision-making and complex task completion in enterprise environments
  • Deploy and manage LLM-based applications with advanced model serving techniques, ensuring scalability, cost-efficiency, and continuous improvement through CI/CD

Prerequisites

  • Practical programming skills in Python and familiarity with LLM concepts and frameworks (3+ Months LLM, 6+ Months Python and Machine Learning)
    • LLM Access via API, Open Source Libraries (HuggingFace)
    • LLM Application development experience (RAG, Chatbots, etc)
  • Familiarity with deep learning concepts and frameworks (e.g., TensorFlow, PyTorch)
  • Experience with software development practices, system design, and enterprise application architecture recommended
  • CI/CD Pipelines and monitoring for traditional ML models (MLOps) recommended

Outline

Expand All | Collapse All

Deep Dive into Enterprise-Grade Chatbot Architectures
  • Designing and implementing advanced chatbot architectures using LLMs
    • Leveraging multi-turn conversation management and context tracking techniques
    • Implementing personalized and adaptive chatbot interactions based on user profiles
  • Integrating chatbots with enterprise systems and workflows
    • Strategies for integrating chatbots with CRM (customer relationship management), ERP (enterprise resource planning), and other enterprise applications
    • Implementing secure authentication and authorization mechanisms for chatbot interactions
  • Building an enterprise-grade chatbot using advanced LLM architectures
    • Designing and implementing a multi-turn, context-aware chatbot architecture
    • Integrating the chatbot with enterprise systems and implementing security measures
Advanced Agentic RAG Architectures and Techniques
  • Exploring advanced Agentic RAG architectures and design patterns
    • Implementing multi-hop reasoning and iterative query refinement techniques in RAG
    • Leveraging graph-based knowledge representations and reasoning in Agentic RAG
  • Optimizing Agentic RAG performance and scalability
    • Implementing distributed retrieval and generation techniques for large-scale Agentic RAG
    • Leveraging caching, pruning, and other optimization techniques for efficient Agentic RAG inference
  • Implementing an advanced Agentic RAG architecture for a specific use case
    • Designing and implementing a multi-hop Agentic RAG architecture with graph-based reasoning
    • Optimizing the Agentic RAG implementation for performance and scalability
Designing and Implementing LLM-Powered Agents and Workflows
  • Designing LLM-powered agents for autonomous decision-making and task completion
    • Implementing goal-oriented and adaptive agent architectures using LLMs
    • Leveraging reinforcement learning and planning techniques for agent decision-making
  • Orchestrating multi-agent workflows and interactions in enterprise environments
    • Designing and implementing multi-agent communication and coordination protocols
    • Implementing fault-tolerant and scalable multi-agent workflows using serverless architectures
  • Building an LLM-powered agent-based workflow for a specific enterprise use case
    • Designing and implementing a goal-oriented, adaptive agent architecture using LLMs
    • Orchestrating a multi-agent workflow using serverless technologies and coordination protocols
Advanced Model Serving and Deployment Techniques
  • Exploring advanced model serving architectures and design patterns
    • Implementing model versioning, A/B testing
    • Leveraging serverless and edge computing for low-latency and cost-efficient model serving
  • Implementing CI/CD pipelines for automated model deployment and monitoring
    • Designing and implementing end-to-end CI/CD pipelines for LLM-based applications
    • Integrating model performance monitoring and drift detection into CI/CD workflows
  • Implementing an advanced model serving architecture with CI/CD for an LLM-based application
    • Designing and implementing a serverless model serving architecture with versioning and A/B testing
  • Setting up a CI/CD pipeline for automated model deployment and monitoring
Conclusion

Training Materials

All Generative AI training students receive comprehensive courseware.

Software Requirements

All attendees must have a modern web browser and an Internet connection.



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