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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.
Principles of Agent Design
- Defining the components of an AI agent.
- Establishing agent roles, memory structures, and tool usage.
- Distinguishing between enterprise-focused and developer-centric agents.
Practical Application: Mistral Medium 3
- Model setup and configuration strategies.
- Tuning inference and optimizing performance.
- Managing multimodal and coding workflows.
Development with Devstral
- Code-first agent architecture.
- Leveraging Devstral for enhanced code understanding.
- Best practices for engineering assistants.
Integrating Le Chat Enterprise
- Deploying Le Chat for enterprise agent solutions.
- Implementing RBAC, SSO, and compliance frameworks.
- Linking enterprise applications and data stores.
End-to-End Agent Workflows
- Synthesizing Mistral Medium 3, Devstral, and Le Chat.
- Constructing multi-tool workflows involving connectors, APIs, and data sources.
- Applying grounding and RAG patterns.
Deployment and Governance
- Comparing self-hosted vs. API-based deployments.
- Monitoring, logging, and observability strategies.
- Addressing cost, performance, and compliance considerations.
Summary and Future Directions
Requirements
- Proficiency in Python programming.
- Practical experience with machine learning workflows.
- Familiarity with APIs and model integration.
Target Audience
- AI Engineers
- Solution Architects
- Applied ML Teams
- Product Developers
14 Hours
