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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 and positioning within the agentic AI landscape
- Principal features and unique differentiators
Principles of Agent Design
- Defining the characteristics of an AI agent
- Establishing agent roles, memory structures, and tool usage
- Distinguishing between enterprise-focused and developer-centric agents
Practical Application with Mistral Medium 3
- Model configuration and setup procedures
- Tuning and optimizing inference performance
- Handling multimodal and coding-related workflows
Development with Devstral
- Designing code-first agent architectures
- Incorporating Devstral for enhanced code comprehension
- Best practices for engineering assistant implementations
Le Chat Enterprise Integration
- Deploying Le Chat to support enterprise-level agents
- Implementing RBAC, SSO, and compliance frameworks
- Linking enterprise applications and data repositories
Comprehensive Agent Workflows
- Synthesizing Mistral Medium 3, Devstral, and Le Chat capabilities
- Constructing multi-tool workflows involving connectors, APIs, and data sources
- Implementing grounding and RAG patterns
Deployment Strategies and Governance
- Comparing self-hosting versus API-based deployment
- Establishing monitoring, logging, and observability mechanisms
- Addressing cost, performance, and compliance factors
Summary and Future Directions
Requirements
- A solid grasp of Python programming
- Practical experience with machine learning workflows
- Proficiency in working with APIs and model integration
Target Audience
- AI Engineers
- Solution Architects
- Applied Machine Learning Teams
- Product Developers
