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 Duration 14 hours

Course Outline

Overview of the Mistral AI Platform

  • Summary of Mistral model capabilities (Medium 3, Le Chat Enterprise, Devstral)
  • Integration within the agentic AI landscape
  • Distinctive features and strategic differentiators

Principles of AI Agent Design

  • Defining core components of an AI agent
  • Establishing agent roles, memory structures, and tooling
  • Differentiating enterprise-grade versus developer-focused agents

Practical Application of Mistral Medium 3

  • Model initialization and configuration
  • Inference tuning and performance optimization
  • Multimodal and software development workflows

Development with Devstral

  • Code-centric agent architecture
  • Utilizing Devstral for advanced code analysis
  • Best practices for engineering assistance

Le Chat Enterprise Integration

  • Implementation of Le Chat for enterprise agent deployment
  • Incorporation of RBAC, SSO, and regulatory compliance
  • Integration of enterprise applications and data repositories

Comprehensive Agent Workflows

  • Synergizing Mistral Medium 3, Devstral, and Le Chat
  • Construction of multi-tool workflows (connectors, APIs, data sources)
  • Contextual grounding and RAG implementation patterns

Deployment Strategy and Governance

  • Self-hosted versus API-based deployment models
  • Surveillance, logging, and observability frameworks
  • Assessment of cost efficiency, performance, and compliance

Concluding Remarks and Strategic Next Steps

Requirements

  • Proficiency in Python programming
  • Practical experience with machine learning workflows
  • Working knowledge of APIs and model integration

Target Audience

  • AI engineers
  • Solution architects
  • Applied ML teams
  • Product developers

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