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Duration 14 hours
Course Outline
Introduction to the Mistral AI Ecosystem
- Survey of Mistral models, including Medium 3, Le Chat Enterprise, and Devstral
- Placement within the broader agentic AI landscape
- Highlighting core features and unique selling points
Principles of Agent Architecture
- Defining the essence of an AI agent
- Establishing agent roles, memory structures, and tool sets
- Distinguishing between enterprise-focused and developer-centric agents
Practical Application with Mistral Medium 3
- Initial setup and configuration processes
- Refining inference and optimizing performance
- Managing multimodal and coding-oriented workflows
Developing with Devstral
- Designing code-centric agents
- Leveraging Devstral for advanced code comprehension
- Adopting best practices for engineering assistants
Integrating Le Chat Enterprise
- Rolling out Le Chat for enterprise agent solutions
- Implementing RBAC, SSO, and compliance frameworks
- Linking enterprise applications and data repositories
Comprehensive Agent Workflows
- Synthesizing Mistral Medium 3, Devstral, and Le Chat
- Constructing multi-tool workflows involving connectors, APIs, and diverse data sources
- Applying grounding techniques and RAG patterns
Deployment Strategies and Governance
- Comparing self-hosting versus API-based deployment
- Establishing monitoring, logging, and observability standards
- Addressing cost efficiency, performance metrics, and regulatory compliance
Conclusion and Future Directions
Requirements
- Solid proficiency in Python programming
- Practical experience with machine learning workflows
- Knowledge of API structures and model integration
Target Audience
- AI Engineers
- Solution Architects
- Applied Machine Learning Teams
- Product Developers
