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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 unique value propositions
Principles of Agent Design
- Defining the characteristics of an AI agent
- Establishing agent roles, memory structures, and tool usage
- Distinguishing between enterprise and developer-centric agents
Practical Application with Mistral Medium 3
- Model configuration and setup
- Tuning and optimizing inference processes
- Multimodal and coding workflow implementations
Development with Devstral
- Code-centric agent architecture
- Integrating Devstral for code comprehension
- Best practices for engineering assistants
Integration with Le Chat Enterprise
- Deploying Le Chat for enterprise-level agents
- Implementing RBAC, SSO, and compliance standards
- Linking enterprise applications and data repositories
End-to-End Agent Workflows
- Synthesizing Mistral Medium 3, Devstral, and Le Chat
- Constructing multi-tool workflows involving connectors, APIs, and data sources
- Grounding techniques and RAG patterns
Deployment and Governance
- Comparing self-hosting versus API deployment strategies
- Monitoring, logging, and observability practices
- Considerations for cost, performance, and compliance
Summary and Future Directions
Requirements
- Proficiency in Python programming
- Experience with machine learning workflows
- Familiarity with APIs and model integration
Audience
- AI engineers
- Solution architects
- Applied ML teams
- Product developers
14 Hours
