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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 competitive differentiators
Agent Design Principles
- Defining the core components of an AI agent
- Structuring agent roles, memory, and tool usage
- Distinguishing between enterprise and developer-focused agents
Hands-On with Mistral Medium 3
- Model setup and configuration
- Tuning inference and optimizing performance
- Managing multimodal and coding workflows
Building with Devstral
- Code-first agent architecture
- Leveraging Devstral for advanced code understanding
- Best practices for engineering assistants
Le Chat Enterprise Integration
- Deploying Le Chat for enterprise-grade agents
- Integrating RBAC, SSO, and compliance frameworks
- Connecting enterprise applications and data repositories
End-to-End Agent Workflows
- Synergizing Mistral Medium 3, Devstral, and Le Chat
- Constructing multi-tool workflows using connectors, APIs, and data sources
- Implementing grounding and RAG patterns
Deployment and Governance
- Evaluating self-hosting versus API deployment
- Establishing monitoring, logging, and observability
- Addressing cost, performance, and compliance considerations
Summary and Next Steps
Requirements
- Proficiency in 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
