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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 landscape.
  • Key features and unique differentiators.

Principles of Agent Design

  • Core characteristics of an AI agent.
  • Defining agent roles, memory structures, and tools.
  • Distinguishing between enterprise-focused and developer-centric agents.

Practical Application with Mistral Medium 3

  • Model setup and configuration.
  • Inference tuning and optimization techniques.
  • Utilizing multimodal and coding workflows.

Development with Devstral

  • Code-first approach to agent design.
  • Integrating Devstral for enhanced code understanding.
  • Best practices for engineering assistance.

Integrating Le Chat Enterprise

  • Deploying Le Chat for enterprise-grade agents.
  • Implementing RBAC, SSO, and compliance measures.
  • Connecting enterprise applications and data repositories.

End-to-End Agent Workflows

  • Synergizing Mistral Medium 3, Devstral, and Le Chat.
  • Constructing multi-tool workflows involving connectors, APIs, and data sources.
  • Applying grounding and RAG (Retrieval-Augmented Generation) patterns.

Deployment and Governance

  • Comparing self-hosting with API deployment.
  • Ensuring monitoring, logging, and observability.
  • Addressing cost, performance, and compliance factors.

Summary and Future Steps

Requirements

  • Proficiency in Python programming.
  • Practical 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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