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Course Outline
The Mistral AI Ecosystem Explained
- Overview of Mistral models, including Medium 3, Le Chat Enterprise, and Devstral
- The role of these models within the broader agentic AI landscape
- Highlighting key features and unique value propositions
Foundations of Agent Design
- Defining the core components of an AI agent
- Establishing agent roles, memory structures, and tool interactions
- Distinguishing between enterprise-focused and developer-centric agent designs
Practical Application with Mistral Medium 3
- Initial model configuration and setup
- Strategies for inference tuning and performance optimization
- Handling multimodal and coding-specific workflows
Development with Devstral
- Designing code-first agents
- Leveraging Devstral for advanced code understanding
- Best practices for building engineering assistants
Integrating Le Chat Enterprise
- Deployment strategies for enterprise-grade agents
- Implementing RBAC, SSO, and compliance frameworks
- Linking enterprise applications and data repositories
Creating Comprehensive Agent Workflows
- Synthesizing capabilities from Mistral Medium 3, Devstral, and Le Chat
- Constructing multi-tool workflows involving connectors, APIs, and diverse data sources
- Applying grounding and RAG patterns effectively
Deployment Strategies and Governance
- Evaluating self-hosted solutions versus API-based deployment
- Establishing monitoring, logging, and observability standards
- Addressing cost efficiency, performance metrics, and compliance requirements
Conclusion and Future Directions
Requirements
- Proficiency in Python programming
- Practical experience with machine learning workflows
- Knowledge of API structures and model integration strategies
Target Audience
- AI engineers
- Solution architects
- Applied ML teams
- Product developers
14 Hours
