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Course Outline
Introduction to the Mistral AI Ecosystem
- Overview of Mistral models (Medium 3, Le Chat Enterprise, Devstral)
- Role within the agentic AI ecosystem
- Core features and unique value propositions
Principles of Agent Design
- Defining the components of an AI agent
- Establishing agent roles, memory structures, and toolsets
- Distinguishing between enterprise and developer-centric agents
Practical Application of Mistral Medium 3
- Setting up and configuring the model
- Tuning inference and optimising performance
- Managing multimodal and coding workflows
Development with Devstral
- Designing code-first agents
- Utilising Devstral for code comprehension
- Best practices for engineering assistants
Integration with Le Chat Enterprise
- Deploying Le Chat for enterprise-level agents
- Implementing RBAC, SSO, and compliance frameworks
- Linking enterprise applications and data repositories
End-to-End Agent Workflows
- Synthesising Mistral Medium 3, Devstral, and Le Chat
- Creating multi-tool workflows involving connectors, APIs, and data sources
- Implementing grounding and RAG patterns
Deployment and Governance
- Comparing self-hosting versus API-based deployment
- Monitoring, logging, and observability strategies
- Addressing cost, performance, and compliance considerations
Conclusion and Further Learning
Requirements
- A solid understanding of Python programming
- Practical experience with machine learning workflows
- Knowledge of APIs and model integration
Target Audience
- AI engineers
- Solution architects
- Applied ML teams
- Product developers
14 Hours