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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
  • Core features and unique selling points

Principles of Agent Design

  • Defining the characteristics of an AI agent
  • Establishing agent roles, memory structures, and toolsets
  • Distinguishing between enterprise and developer-focused agents

Practical Session with Mistral Medium 3

  • Model configuration and setup
  • Tuning and optimizing inference processes
  • Managing multimodal and coding workflows

Development with Devstral

  • Code-centric agent architecture
  • Leveraging Devstral for code comprehension
  • Best practices for engineering assistants

Integrating Le Chat Enterprise

  • Implementing Le Chat for enterprise-grade agents
  • Incorporating RBAC, SSO, and compliance frameworks
  • Linking enterprise applications and data repositories

End-to-End Agent Workflows

  • Synergizing 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

  • Comparing self-hosting against API-based deployment
  • Establishing monitoring, logging, and observability
  • Addressing cost, performance, and compliance requirements

Conclusion and Future Steps

Requirements

  • A solid grasp of Python programming
  • Practical experience with machine learning pipelines
  • Working knowledge of APIs and model integration

Target Audience

  • AI Engineers
  • Solution Architects
  • Applied ML Teams
  • Product Developers

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