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Duration 14 hours
Course Outline
Introduction to the Mistral AI Ecosystem
- Overview of Mistral models (Medium 3, Le Chat Enterprise, Devstral)
- Positioning within the agentic AI ecosystem
- Key features and unique differentiators
Agent Design Principles
- Defining the core attributes of an AI agent
- Establishing agent roles, memory structures, and tools
- Distinguishing between enterprise and developer-centric agents
Hands-On Exploration of Mistral Medium 3
- Model setup and configuration
- Inference tuning and performance optimisation
- Multimodal and coding workflows
Building with Devstral
- Code-first agent design strategies
- Integrating Devstral for enhanced code understanding
- Best practices for engineering assistants
Le Chat Enterprise Integration
- Deploying Le Chat for enterprise-grade agents
- Implementing RBAC, SSO, and compliance requirements
- Connecting enterprise applications and data stores
End-to-End Agent Workflows
- Combining Mistral Medium 3, Devstral, and Le Chat
- Constructing multi-tool workflows involving connectors, APIs, and data sources
- Implementing grounding and RAG patterns
Deployment and Governance
- Comparing self-hosting versus API deployment
- Monitoring, logging, and observability practices
- Considering cost, performance, and compliance factors
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
