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Duration 14 hours
Course Outline
Introduction to the Mistral AI Ecosystem
- Survey of Mistral models (Medium 3, Le Chat Enterprise, Devstral)
- Role within the agentic AI landscape
- Core features and unique advantages
Principles of Agent Design
- Defining the characteristics of an AI agent
- Establishing agent roles, memory structures, and toolsets
- Enterprise-focused vs. developer-centric agents
Practical Work with Mistral Medium 3
- Model initialization and setup
- Tuning and optimizing inference
- Workflows for multimodal and coding tasks
Development with Devstral
- Code-centric agent design
- Incorporating Devstral for code comprehension
- Best practices for engineering assistants
Integration with Le Chat Enterprise
- Implementing Le Chat for enterprise agents
- Integrating RBAC, SSO, and compliance standards
- Linking enterprise applications and data repositories
Comprehensive Agent Workflows
- Synthesizing Mistral Medium 3, Devstral, and Le Chat
- Constructing multi-tool workflows (connectors, APIs, data sources)
- Implementing grounding and RAG patterns
Deployment and Governance
- Comparing self-hosting vs. API deployment
- Monitoring, logging, and observability strategies
- Factors regarding cost, performance, and compliance
Conclusion and Future Directions
Requirements
- Proficiency in Python programming
- Background in machine learning workflows
- Knowledge of APIs and model integration
Intended Audience
- AI engineers
- Solution architects
- Applied ML teams
- Product developers
