Course Outline

Introduction to Open-Source LLMs

  • Overview of DeepSeek, Mistral, LLaMA, and other open-source models
  • How LLMs work: Transformers, self-attention, and training
  • Comparing open-source LLMs vs. proprietary models

Fine-Tuning and Customizing LLMs

  • Data preparation for fine-tuning
  • Training and optimizing LLMs using Hugging Face
  • Evaluating model performance and bias mitigation

Building AI Agents with LLMs

  • Introduction to LangChain for AI agent development
  • Designing agent-based workflows with LLMs
  • Memory, retrieval-augmented generation (RAG), and action execution

Deploying LLM-Based AI Agents

  • Containerizing AI agents with Docker
  • Integrating LLMs into enterprise applications
  • Scaling AI agents with cloud services and APIs

Security and Compliance in Enterprise AI

  • Ethical considerations and regulatory compliance
  • Mitigating risks in AI-driven automation
  • Monitoring and auditing AI agent behavior

Case Studies and Real-World Applications

  • LLM-powered virtual assistants
  • AI-driven document automation
  • Custom AI agents for enterprise analytics

Optimizing and Maintaining LLM-Based Agents

  • Continuous model improvement and updating
  • Deploying monitoring and feedback loops
  • Strategies for cost optimization and performance tuning

Summary and Next Steps

Requirements

  • Strong understanding of AI and machine learning
  • Experience with Python programming
  • Familiarity with large language models (LLMs) and natural language processing (NLP)

Audience

  • AI engineers
  • Enterprise software developers
  • Business leaders
 21 Hours

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