LLM-Based AI Agents for Enterprise Automation Training Course
With the rise of open-source models like DeepSeek, Mistral, and LLaMA, enterprises are deploying custom AI agents for various workflows.
This instructor-led, live training (online or onsite) is aimed at advanced-level AI engineers, enterprise software developers, and business leaders who wish to customize and deploy LLM-based AI agents for enterprise applications.
By the end of this training, participants will be able to:
- Understand the architecture and capabilities of open-source LLMs.
- Customize and fine-tune LLMs for enterprise use cases.
- Deploy AI agents using LangChain and Hugging Face.
- Integrate LLM-powered agents into business workflows.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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
Open Training Courses require 5+ participants.
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