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Duration 14 hours
Course Outline
Getting to know the Mistral AI Ecosystem
- Survey of Mistral models (Medium 3, Le Chat Enterprise, Devstral)
- Placement within the broader agentic AI landscape
- Distinctive features and value propositions
Core Principles of Agent Design
- Defining the characteristics of an AI agent
- Structuring agent roles, memory mechanisms, and toolsets
- Differentiating enterprise-focused versus developer-centric agents
Practical Exploration of Mistral Medium 3
- Initial setup and configuration of the model
- Tuning and optimizing inference processes
- Handling multimodal and coding-focused tasks
Development with Devstral
- Designing agents with a code-first approach
- Utilizing Devstral for advanced code analysis
- Best practices for engineering assistant tools
Integrating Le Chat Enterprise
- Rolling out Le Chat for enterprise agent applications
- Implementing RBAC, SSO, and compliance protocols
- Linking enterprise applications with data repositories
Complete Agent Workflows
- Synergizing Mistral Medium 3, Devstral, and Le Chat
- Constructing multi-tool workflows using connectors, APIs, and data sources
- Implementing grounding techniques and RAG patterns
Deployment Strategy and Governance
- Choosing between self-hosting and API-based deployment
- Establishing monitoring, logging, and observability standards
- Balancing cost, performance, and regulatory compliance
Wrap-up and Future Directions
Requirements
- Foundational knowledge of Python programming
- Practical experience with machine learning workflows
- Proficiency with API interactions and model integration
Intended Learners
- AI Engineers
- Solution Architects
- Applied Machine Learning Teams
- Product Developers
