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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)
- Positioning within the agentic AI landscape
- Key features and distinct advantages
Principles of Agent Design
- Defining the characteristics of an AI agent
- Establishing agent roles, memory structures, and tool usage
- Distinguishing between enterprise and developer-centric agents
Practical Work with Mistral Medium 3
- Model configuration and setup
- Inference tuning and performance optimization
- Multimodal and coding workflows
Development with Devstral
- Code-first agent architecture
- Integrating Devstral for code comprehension
- Best practices for engineering assistants
Integration of Le Chat Enterprise
- Deploying Le Chat for enterprise-level agents
- Implementation of RBAC, SSO, and compliance protocols
- Connecting enterprise applications and data repositories
End-to-End Agent Workflows
- Synthesizing Mistral Medium 3, Devstral, and Le Chat
- Constructing multi-tool workflows using connectors, APIs, and data sources
- Implementing grounding and RAG patterns
Deployment and Governance
- Comparison of self-hosting versus API deployment
- Monitoring, logging, and observability strategies
- Considerations for cost, performance, and compliance
Summary and Future Steps
Requirements
- A solid understanding of Python programming
- Practical experience with machine learning workflows
- Familiarity with API usage and model integration
Target Audience
- AI engineers
- Solution architects
- Applied ML teams
- Product developers
