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Duration 14 hours
Course Outline
Introduction to the Mistral AI Ecosystem
- Overview of the Mistral model lineup (Medium 3, Le Chat Enterprise, Devstral)
- Strategic positioning within the agentic AI landscape
- Defining features and competitive advantages
Principles of Agent Design
- Defining the core components of an AI agent
- Establishing agent roles, memory structures, and toolsets
- Distinguishing between enterprise-oriented and developer-centric agents
Practical Application of Mistral Medium 3
- Initial model setup and configuration strategies
- Optimizing inference processes for performance
- Implementing multimodal and coding-centric workflows
Development with Devstral
- Architecting code-first agent solutions
- Integrating Devstral to enhance code comprehension
- Best practices for engineering assistance tools
Integrating Le Chat Enterprise
- Rolling out Le Chat for enterprise-level agent solutions
- Implementing RBAC, SSO, and compliance frameworks
- Linking enterprise applications with underlying data stores
Comprehensive Agent Workflows
- Synergizing Mistral Medium 3, Devstral, and Le Chat
- Constructing multi-tool workflows using connectors, APIs, and data sources
- Applying grounding techniques and RAG patterns
Deployment and Governance
- Comparing self-hosted environments versus API-based deployments
- Establishing monitoring, logging, and observability protocols
- Addressing cost efficiency, performance metrics, and regulatory compliance
Conclusion and Future Directions
Requirements
- A solid grasp of Python programming
- Practical experience with machine learning workflows
- Knowledge of API mechanics and model integration
Target Audience
- AI Engineers
- Solution Architects
- Applied ML Teams
- Product Developers
