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Course Outline
Introduction to the Mistral AI Ecosystem
- Overview of Mistral models, including Medium 3, Le Chat Enterprise, and Devstral
- Positioning within the agentic AI landscape
- Core features and competitive differentiators
Principles of Agent Design
- Defining the characteristics of an AI agent
- Establishing agent roles, memory structures, and tool usage
- Distinguishing between enterprise and developer-centric agents
Practical Work with Mistral Medium 3
- Model setup and configuration procedures
- Tuning inference and optimizing performance
- Handling multimodal and coding workflows
Development with Devstral
- Code-first approaches to agent design
- Integrating Devstral for enhanced code understanding
- Best practices for engineering assistants
Integration with Le Chat Enterprise
- Deploying Le Chat for enterprise-grade agents
- Implementing RBAC, SSO, and compliance standards
- Linking enterprise applications and data repositories
End-to-End Agent Workflows
- Synergizing Mistral Medium 3, Devstral, and Le Chat
- Creating multi-tool workflows involving connectors, APIs, and data sources
- Applying grounding and RAG patterns
Deployment and Governance
- Comparing self-hosting versus API deployment strategies
- Implementing monitoring, logging, and observability measures
- Addressing cost, performance, and compliance factors
Summary and Future Directions
Requirements
- A solid understanding of Python programming
- Practical experience with machine learning workflows
- Familiarity with APIs and model integration strategies
Target Audience
- AI engineers
- Solution architects
- Applied ML teams
- Product developers
14 Hours
