WA3510
Developing Advanced LLM Applications Training
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.
Course Details
Duration
4 days
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
Skills Gained
- Design and implement advanced chatbot architectures using LLMs and enterprise system integration
- Implement advanced Agentic RAG architectures and techniques for complex reasoning and knowledge retrieval
- Design and implement LLM-powered agents and multi-agent workflows for autonomous decision-making and task completion
- Apply advanced model serving and deployment techniques, including CI/CD pipelines and monitoring
Course Outline
- 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, ERP, 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
- Designing and implementing advanced chatbot architectures using LLMs
- 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
- Exploring advanced Agentic RAG architectures and design patterns
- 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
- Designing LLM-powered agents for autonomous decision-making and task completion
- 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
- Exploring advanced model serving architectures and design patterns
