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Duration 14 hours
Course Outline
Foundations of the Mistral AI Ecosystem
- Overview of Mistral models (Medium 3, Le Chat Enterprise, Devstral)
- Role within the agentic AI landscape
- Primary features and competitive advantages
Principles of Agent Design
- Core components of an AI agent
- Establishing agent roles, memory structures, and toolsets
- Distinguishing between enterprise-focused and developer-centric agents
Practical Application of Mistral Medium 3
- Initial model setup and configuration
- Adjusting inference parameters for optimization
- Workflows for multimodal and coding tasks
Development with Devstral
- Designing code-first agents
- Utilizing Devstral for deep code understanding
- Best practices for engineering assistance
Integrating Le Chat Enterprise
- Implementing Le Chat for enterprise agent solutions
- Incorporating RBAC, SSO, and compliance standards
- Linking enterprise applications and data repositories
Comprehensive Agent Workflows
- Synergizing Mistral Medium 3, Devstral, and Le Chat
- Creating multi-tool workflows involving connectors, APIs, and data sources
- Implementing grounding and RAG patterns
Deployment Strategies and Governance
- Comparing self-hosted versus API-based deployment
- Establishing monitoring, logging, and observability systems
- Addressing cost efficiency, performance, and compliance factors
Recap and Future Directions
Requirements
- Proficiency in Python programming
- Background in machine learning workflows
- Knowledge of APIs and model integration
Target Audience
- AI Engineers
- Solution Architects
- Applied Machine Learning Teams
- Product Developers
