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Duration 14 hours
Course Outline
Introduction to the Mistral AI Ecosystem
- Overview of Mistral models (Medium 3, Le Chat Enterprise, Devstral)
- Role within the agentic AI landscape
- Distinct features and competitive advantages
Principles of Agent Design
- Defining what constitutes an AI agent
- Establishing agent roles, memory structures, and toolsets
- Distinguishing enterprise-focused vs. developer-centric agents
Practical Work with Mistral Medium 3
- Model initialization and setup
- Optimizing inference and performance tuning
- Handling multimodal and coding tasks
Development with Devstral
- Code-first approaches to agent architecture
- Utilizing Devstral for code comprehension
- Best practices for engineering assistants
Integrating Le Chat Enterprise
- Implementing Le Chat for enterprise-level agents
- Integrating RBAC, SSO, and compliance frameworks
- Linking enterprise applications and data repositories
End-to-End Agent Workflows
- Synthesizing Mistral Medium 3, Devstral, and Le Chat
- Creating multi-tool workflows (connectors, APIs, data sources)
- Applying grounding and RAG patterns
Deployment and Governance
- Comparing self-hosting vs. API-based deployment
- Implementing monitoring, logging, and observability
- Addressing cost, performance, and compliance factors
Conclusion and Future Directions
Requirements
- Proficiency in Python programming
- Experience with machine learning workflows
- Knowledge of APIs and model integration
Target Audience
- AI Engineers
- Solution Architects
- Applied ML Teams
- Product Developers
