Course Outline

Introduction to Mistral AI Ecosystem for Government

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

Agent Design Principles for Government

  • Characteristics of an AI agent
  • Defining agent roles, memory, and tools
  • Enterprise versus developer-centric agents

Hands-On with Mistral Medium 3 for Government

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

Building with Devstral for Government

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

Le Chat Enterprise Integration for Government

  • Deploying Le Chat for enterprise agents
  • Role-Based Access Control (RBAC), Single Sign-On (SSO), and compliance integration
  • Connecting enterprise applications and data stores

End-to-End Agent Workflows for Government

  • Combining Mistral Medium 3, Devstral, and Le Chat
  • Building multi-tool workflows (connectors, APIs, data sources)
  • Grounding and Retrieval-Augmented Generation (RAG) patterns

Deployment and Governance for Government

  • Self-hosting versus API deployment
  • Monitoring, logging, and observability
  • Cost, performance, and compliance considerations

Summary and Next Steps for Government

Requirements

  • Proficiency in Python programming
  • Experience with machine learning processes
  • Knowledge of APIs and model integration

Audience

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

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