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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
- Key features and competitive differentiators
Agent Design Principles
- Defining the components of an AI agent
- Establishing agent roles, memory structures, and tool sets
- Distinguishing between enterprise-focused and developer-centric agents
Hands-On with Mistral Medium 3
- Model setup and configuration processes
- Tuning inference and optimizing performance
- Exploring multimodal and coding workflows
Building with Devstral
- Code-first agent design strategies
- Leveraging Devstral for enhanced code comprehension
- Best practices for engineering assistant applications
Le Chat Enterprise Integration
- 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 in unified solutions
- Constructing multi-tool workflows involving connectors, APIs, and data sources
- Implementing grounding and RAG patterns
Deployment and Governance
- Comparing self-hosting versus API-based deployment strategies
- Monitoring, logging, and observability practices
- Considering cost, performance, and compliance implications
Summary and Next Steps
Requirements
- Solid knowledge of Python programming
- Practical experience with machine learning workflows
- Proficiency in working with APIs and model integration
Target Audience
- AI Engineers
- Solution Architects
- Applied Machine Learning Teams
- Product Developers
14 Hours
