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Course Outline

Introduction to the Mistral AI Ecosystem

  • Overview of Mistral models (Medium 3, Le Chat Enterprise, Devstral).
  • Positioning within the agentic AI ecosystem.
  • Key features and differentiators.

Principles of Agent Design

  • Elements that define an AI agent.
  • Defining agent roles, memory, and tools.
  • Distinctions between enterprise-focused and developer-centric agents.

Practical Application with Mistral Medium 3

  • Model setup and configuration.
  • Inference tuning and optimization.
  • Multimodal and coding workflows.

Development with Devstral

  • Code-first agent design.
  • Integrating Devstral for code comprehension.
  • Best practices for engineering assistants.

Integration of Le Chat Enterprise

  • Deploying Le Chat for enterprise agents.
  • Implementation of RBAC, SSO, and compliance.
  • Linking enterprise applications and data repositories.

End-to-End Agent Workflows

  • Synthesizing Mistral Medium 3, Devstral, and Le Chat.
  • Constructing multi-tool workflows (connectors, APIs, data sources).
  • Grounding and RAG patterns.

Deployment and Governance

  • Self-hosting versus API deployment.
  • Monitoring, logging, and observability.
  • Considerations for cost, performance, and compliance.

Summary and Next Steps

Requirements

  • Proficiency in Python programming.
  • Prior experience with machine learning workflows.
  • Familiarity with APIs and model integration.

Target Audience

  • AI engineers.
  • Solution architects.
  • Applied ML teams.
  • Product developers.
 14 Hours

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