LLM-Based AI Agents for Enterprise Automation Training Course
With the emergence of open-source models such as DeepSeek, Mistral, and LLaMA, organizations are increasingly deploying custom AI agents for various workflows.
This instructor-led, live training (available online or on-site) is designed for advanced-level AI engineers, enterprise software developers, and business leaders who aim to customize and deploy LLM-based AI agents for government and enterprise applications.
By the end of this training, participants will be able to:
- Comprehend the architecture and capabilities of open-source LLMs.
- Customize and fine-tune LLMs for specific use cases in government and enterprise environments.
- Deploy AI agents using platforms like LangChain and Hugging Face.
- Integrate LLM-powered agents into organizational workflows.
Format of the Course
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for government or enterprise needs, please contact us to arrange.
Course Outline
Introduction to Open-Source LLMs
- Overview of DeepSeek, Mistral, LLaMA, and other open-source models for government use
- How LLMs work: Transformers, self-attention mechanisms, and training processes
- Comparing open-source LLMs to proprietary models in the context of government applications
Fine-Tuning and Customizing LLMs for Government Use
- Data preparation for fine-tuning LLMs to meet specific governmental needs
- Training and optimizing LLMs using Hugging Face tools and resources
- Evaluating model performance and implementing strategies for bias mitigation
Building AI Agents with LLMs for Government Applications
- Introduction to LangChain for developing AI agents tailored for government operations
- Designing agent-based workflows that integrate LLMs into public sector processes
- Implementing memory, retrieval-augmented generation (RAG), and action execution in governmental AI systems
Deploying LLM-Based AI Agents for Government Use
- Containerizing AI agents with Docker to ensure scalability and security
- Integrating LLMs into enterprise applications for government agencies
- Scaling AI agents using cloud services and APIs to support large-scale governmental operations
Security and Compliance in Enterprise AI for Government
- Ethical considerations and regulatory compliance for government AI systems
- Mitigating risks associated with AI-driven automation in public sector environments
- Monitoring and auditing the behavior of AI agents to ensure accountability and transparency
Case Studies and Real-World Applications for Government
- LLM-powered virtual assistants enhancing citizen services
- AI-driven document automation improving efficiency in government offices
- Custom AI agents for advanced enterprise analytics supporting policy-making and decision support
Optimizing and Maintaining LLM-Based Agents for Government Use
- Continuous model improvement and updating to maintain relevance and accuracy
- Deploying monitoring and feedback loops to enhance system performance
- Strategies for cost optimization and performance tuning in government AI systems
Summary and Next Steps for Government Implementation
Requirements
- Proficient understanding of artificial intelligence and machine learning for government applications
- Experience with Python programming in a governmental context
- Knowledge of large language models (LLMs) and natural language processing (NLP) for government use
Audience
- AI engineers working in the public sector
- Enterprise software developers supporting government projects
- Business leaders involved in governmental initiatives
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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