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Duration 14 hours
Course Outline
Introduction to the Mistral AI Ecosystem
- Overview of Mistral models (Medium 3, Le Chat Enterprise, Devstral)
- Positioning within the agentic AI ecosystem
- Key features and distinguishing elements
Principles of Agent Design
- Defining the characteristics of an AI agent
- Establishing agent roles, memory structures, and tools
- Differentiating enterprise-focused and developer-centric agents
Hands-On Work with Mistral Medium 3
- Model configuration and setup
- Tuning and optimising inference
- Managing multimodal and coding workflows
Developing with Devstral
- Code-centric agent design
- Integrating Devstral for enhanced code comprehension
- Best practices for engineering assistants
Integrating Le Chat Enterprise
- Deploying Le Chat for enterprise-grade agents
- Incorporating RBAC, SSO, and compliance frameworks
- Linking enterprise applications and data repositories
End-to-End Agent Workflows
- Synthesising Mistral Medium 3, Devstral, and Le Chat
- Creating multi-tool workflows (connectors, APIs, data sources)
- Implementing grounding and RAG patterns
Deployment and Governance
- Self-hosting versus API deployment strategies
- Monitoring, logging, and observability
- Considering cost, performance, and compliance
Summary and Future Steps
Requirements
- A solid understanding of Python programming
- Practical experience with machine learning workflows
- Familiarity with APIs and model integration
Target Audience
- AI engineers
- Solution architects
- Applied ML teams
- Product developers
