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Course Outline

Introduction to Open-Source LLMs

  • Overview of DeepSeek, Mistral, LLaMA, and other open-source models.
  • How LLMs function: Transformers, self-attention mechanisms, and training processes.
  • Comparing open-source LLMs versus proprietary models.

Fine-Tuning and Customizing LLMs

  • Preparing data for fine-tuning.
  • Training and optimizing LLMs using Hugging Face.
  • Evaluating model performance and mitigating bias.

Building AI Agents with LLMs

  • Introduction to LangChain for AI agent development.
  • Designing agent-based workflows with LLMs.
  • Managing 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 using cloud services and APIs.

Security and Compliance in Enterprise AI

  • Ethical considerations and regulatory compliance.
  • Mitigating risks associated with 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 updates.
  • Deploying monitoring and feedback loops.
  • Strategies for cost optimization and performance tuning.

Summary and Next Steps

Requirements

  • A strong grasp of AI and machine learning concepts.
  • Practical experience with Python programming.
  • Familiarity with large language models (LLMs) and natural language processing (NLP).

Target Audience

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

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