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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
Principles of Agent Design
- Defining the components of an AI agent
- Establishing agent roles, memory, and tools
- Distinguishing enterprise versus developer-centric agents
Practical Application with Mistral Medium 3
- Model setup and configuration
- Tuning inference and optimizing performance
- Handling multimodal and coding workflows
Development with Devstral
- Code-first agent design principles
- Integrating Devstral for enhanced code understanding
- Best practices for engineering assistants
Integrating Le Chat Enterprise
- Deploying Le Chat for enterprise-level agents
- Implementing RBAC, SSO, and compliance features
- Connecting enterprise applications and data repositories
End-to-End Agent Workflows
- Synthesizing Mistral Medium 3, Devstral, and Le Chat
- Creating multi-tool workflows involving connectors, APIs, and data sources
- Implementing grounding and RAG patterns
Deployment and Governance
- Comparing self-hosting versus API deployment
- Managing monitoring, logging, and observability
- Addressing cost, performance, and compliance factors
Conclusion and Future Steps
Requirements
- A solid grasp of Python programming
- Experience with machine learning workflows
- Familiarity with APIs and model integration
Target Audience
- AI Engineers
- Solution Architects
- Applied ML Teams
- Product Developers
14 Hours
