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Duration 14 hours
Course Outline
Introduction to the Mistral AI Ecosystem
- Survey of Mistral model offerings (Medium 3, Le Chat Enterprise, Devstral)
- Role within the broader agentic AI landscape
- Distinctive features and competitive advantages
Principles of Agent Design
- Defining the core characteristics of an AI agent
- Establishing agent roles, memory structures, and toolsets
- Distinguishing between enterprise-oriented and developer-centric agents
Practical Application with Mistral Medium 3
- Initial model setup and configuration strategies
- Tuning inference processes and optimizing efficiency
- Managing multimodal and coding-centric workflows
Development with Devstral
- Code-centric approaches to agent architecture
- Utilizing Devstral for enhanced code comprehension
- Best practices for engineering assistant implementation
Integrating Le Chat Enterprise
- Deploying Le Chat to power enterprise-level agents
- Implementing RBAC, SSO, and regulatory compliance frameworks
- Linking enterprise applications and internal data repositories
End-to-End Agent Workflows
- Synergizing Mistral Medium 3, Devstral, and Le Chat
- Constructing multi-tool workflows involving connectors, APIs, and data sources
- Implementing grounding techniques and RAG patterns
Deployment and Governance
- Evaluating self-hosted solutions versus API-based deployment
- Establishing monitoring, logging, and observability standards
- Addressing cost control, performance metrics, and compliance requirements
Conclusion and Future Directions
Requirements
- Proficiency in Python programming
- Practical experience with machine learning workflows
- Knowledge of API structures and model integration techniques
Target Audience
- AI Engineers
- Solution Architects
- Applied Machine Learning Teams
- Product Developers
