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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, and training mechanisms.
  • Comparing open-source LLMs versus proprietary models.

Fine-Tuning and Customising LLMs

  • Data preparation for fine-tuning.
  • Training and optimising 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.
  • Memory, retrieval-augmented generation (RAG), and action execution.

Deploying LLM-Based AI Agents

  • Containerising 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 behaviour.

Case Studies and Real-World Applications

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

Optimising and Maintaining LLM-Based Agents

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

Summary and Next Steps

Requirements

  • Robust understanding of artificial intelligence and machine learning.
  • 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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