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Duration 14 hours
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 competitive differentiators.
Agent Design Principles
- Defining the core components of an AI agent.
- Establishing agent roles, memory structures, and toolsets.
- Distinguishing between enterprise and developer-centric agent designs.
Hands-On with Mistral Medium 3
- Model initialization and configuration strategies.
- Tuning inference for optimal performance.
- Handling multimodal and coding-centric workflows.
Building with Devstral
- Designing code-first agents.
- Integrating Devstral for advanced code comprehension.
- Best practices for engineering assistants.
Le Chat Enterprise Integration
- Deploying Le Chat for enterprise-scale agents.
- Implementing RBAC, SSO, and compliance protocols.
- Linking enterprise applications and data repositories.
End-to-End Agent Workflows
- Synthesizing Mistral Medium 3, Devstral, and Le Chat.
- Creating multi-tool workflows involving connectors, APIs, and data sources.
- Applying grounding and RAG patterns effectively.
Deployment and Governance
- Comparing self-hosting with API deployment models.
- Establishing monitoring, logging, and observability frameworks.
- Addressing cost, performance, and compliance factors.
Summary and Next Steps
Requirements
- Proficiency in Python programming.
- Practical experience with machine learning workflows.
- Knowledge of APIs and model integration processes.
Target Audience
- AI engineers.
- Solution architects.
- Applied Machine Learning teams.
- Product developers.
