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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
- Core features and key differentiators
Agent Design Principles
- Defining the components of an AI agent
- Establishing agent roles, memory structures, and toolsets
- Distinguishing between enterprise and developer-centric agents
Hands-On with Mistral Medium 3
- Model setup and configuration processes
- Inference tuning and performance optimization
- Multimodal and coding workflow implementation
Building with Devstral
- Code-first agent architecture
- Leveraging Devstral for code comprehension
- Best practices for engineering assistants
Le Chat Enterprise Integration
- Deploying Le Chat for enterprise-grade agents
- Implementing RBAC, SSO, and compliance controls
- Linking enterprise applications and data repositories
End-to-End Agent Workflows
- Synthesizing Mistral Medium 3, Devstral, and Le Chat
- Constructing multi-tool workflows (connectors, APIs, data sources)
- Applying grounding and RAG patterns
Deployment and Governance
- Comparing self-hosting with API-based deployment
- Monitoring, logging, and observability strategies
- Balancing cost, performance, and regulatory compliance
Summary and Path Forward
Requirements
- Solid understanding of Python programming
- Practical experience with machine learning workflows
- Proficiency in API interactions and model integration
Target Audience
- AI engineers
- Solution architects
- Applied ML teams
- Product developers
14 Hours
