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Duration 14 hours
Course Outline
Fundamentals of the Mistral AI Ecosystem
- Survey of Mistral models (Medium 3, Le Chat Enterprise, Devstral)
- Role within the agentic AI landscape
- Distinctive features and competitive advantages
Principles of Agent Architecture
- Defining the core components of an AI agent
- Specifying agent responsibilities, memory structures, and toolkits
- Differentiating between enterprise and developer-focused agents
Practical Work with Mistral Medium 3
- Initial setup and configuration strategies
- Optimizing inference processes
- Managing multimodal and coding tasks
Development with Devstral
- Code-centric agent design methods
- Utilizing Devstral for code analysis
- Best practices for engineering assistance
Integrating Le Chat Enterprise
- Implementation of Le Chat for corporate agents
- Setting up RBAC, SSO, and compliance frameworks
- Linking internal applications and data repositories
Complete Agent Lifecycle Workflows
- Synergizing Mistral Medium 3, Devstral, and Le Chat
- Constructing multi-tool pipelines (connectors, APIs, data feeds)
- Applying grounding and RAG methodologies
Deployment Strategies and Governance
- Comparing self-hosted and API-based deployments
- Implementing monitoring, logging, and observability
- Addressing cost, performance, and regulatory requirements
Key Takeaways and Future Directions
Requirements
- Proficiency in Python programming
- Practical experience with machine learning workflows
- Knowledge of API mechanics and model integration
Intended Learners
- AI Engineers
- Solution Architects
- Applied Machine Learning Teams
- Product Developers
