LLM-Based AI Agents for Enterprise Automation Training Course
The growing availability of open-source models such as DeepSeek, Mistral, and LLaMA has enabled enterprises to implement custom AI agents across a variety of operational workflows.
This instructor-led, live training session, available either online or on-site, is designed for advanced AI engineers, enterprise software developers, and business executives looking to tailor and deploy LLM-driven AI agents for organizational use.
Upon completion of this training, participants will be equipped to:
- Grasp the architecture and functional capabilities of open-source LLMs.
- Customize and fine-tune LLMs to meet specific enterprise requirements.
- Deploy AI agents leveraging LangChain and Hugging Face.
- Seamlessly integrate LLM-powered agents into existing business processes.
Course Format
- Engaging lectures and interactive discussions.
- Extensive practical exercises and hands-on practice.
- Real-time implementation within a live-lab environment.
Customization Options
- For tailored training needs, please reach out to us to make arrangements.
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.
Open Training Courses require 5+ participants.
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