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Duration 14 hours
Course Outline
Introduction to the Mistral AI Ecosystem
- Overview of key Mistral models, including Medium 3, Le Chat Enterprise, and Devstral
- Strategic positioning within the broader agentic AI landscape
- Core features and unique differentiators
Principles of Agent Design
- Fundamental components of an AI agent
- Defining agent roles, memory mechanisms, and tooling
- Distinguishing between enterprise-focused and developer-centric agent architectures
Hands-On Workshop: Mistral Medium 3
- Initial model setup and configuration strategies
- Tuning inference parameters for optimal performance
- Managing multimodal and coding-specific workflows
Building Solutions with Devstral
- Code-centric approaches to agent design
- Leveraging Devstral for advanced code comprehension tasks
- Best practices for engineering assistant integration
Integrating Le Chat Enterprise
- Strategies for deploying Le Chat in enterprise agent scenarios
- Implementation of RBAC, SSO, and compliance frameworks
- Connecting to enterprise applications and data repositories
End-to-End Agent Workflows
- Synergizing Mistral Medium 3, Devstral, and Le Chat
- Constructing multi-tool workflows involving connectors, APIs, and diverse data sources
- Implementing grounding and Retrieval-Augmented Generation (RAG) patterns
Deployment and Governance Strategies
- Evaluating self-hosted versus API-based deployment models
- Establishing monitoring, logging, and observability standards
- Addressing cost, performance, and compliance considerations
Summary and Future Directions
Requirements
- A solid foundation in Python programming
- Practical experience with machine learning workflows
- Proficiency in API usage and model integration processes
Target Audience
- AI Engineers
- Solution Architects
- Applied Machine Learning Teams
- Product Developers
