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Course Outline
Introduction to the Mistral AI Ecosystem
- Overview of Mistral models (Medium 3, Le Chat Enterprise, Devstral)
- Positioning within the agentic AI landscape
- Key features and unique differentiators
Principles of Agent Design
- Defining the core components of an AI agent
- Establishing agent roles, memory structures, and tool usage
- Distinguishing between enterprise and developer-centric agents
Practical Application with Mistral Medium 3
- Model setup and configuration strategies
- Tuning and optimizing inference performance
- Managing multimodal and coding workflows
Development with Devstral
- Designing code-first agents
- Integrating Devstral for enhanced code understanding
- Best practices for engineering assistants
Integrating Le Chat Enterprise
- Deploying Le Chat for enterprise-grade agents
- Implementing RBAC, SSO, and compliance standards
- Connecting enterprise applications and data repositories
End-to-End Agent Workflows
- Combining Mistral Medium 3, Devstral, and Le Chat for cohesive solutions
- Creating multi-tool workflows involving connectors, APIs, and data sources
- Applying grounding and RAG patterns
Deployment and Governance
- Evaluating self-hosting versus API deployment strategies
- Implementing monitoring, logging, and observability solutions
- Addressing cost, performance, and compliance considerations
Summary and Future Steps
Requirements
- A solid understanding of Python programming
- Practical experience with machine learning workflows
- Familiarity with APIs and model integration processes
Target Audience
- AI engineers
- Solution architects
- Applied ML teams
- Product developers
14 Hours
