Get in Touch

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

  • Solid foundational knowledge of AI and machine learning concepts.
  • Practical experience in Python programming.
  • Familiarity with large language models (LLMs) and natural language processing (NLP).

Target Audience

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

Number of participants


Price per participant

Upcoming Courses

Related Categories