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Duration 14 hours
Course Outline
Intro to the Mistral AI Ecosystem
- Survey of Mistral models (Medium 3, Le Chat Enterprise, Devstral)
- Role within the agentic AI landscape
- Core features and competitive advantages
Principles of Agent Architecture
- Defining the components of an AI agent
- Establishing agent roles, memory structures, and tool usage
- Distinguishing between enterprise-focused and developer-focused agents
Practical Application of Mistral Medium 3
- Configuration and initial setup of the model
- Adjusting inference parameters and optimizing output
- Workflows for multimodal and coding tasks
Development using Devstral
- Designing agents with a code-centric approach
- Incorporating Devstral for code interpretation
- Best practices for engineering assistants
Integration with Le Chat Enterprise
- Rolling out Le Chat for enterprise-grade agents
- Implementing RBAC, SSO, and compliance measures
- Linking enterprise applications and data repositories
Comprehensive Agent Workflows
- Synthesizing Mistral Medium 3, Devstral, and Le Chat
- Constructing multi-tool processes (connectors, APIs, data sources)
- Applying grounding and RAG patterns
Deployment and Oversight
- Comparing self-hosting with API-based deployment
- Implementing monitoring, logging, and observability
- Addressing cost, performance, and compliance factors
Wrap-Up and Future Directions
Requirements
- A solid grasp of Python programming
- Practical experience with machine learning pipelines
- Knowledge of API structures and model integration
Target Audience
- AI engineers
- Solution architects
- Applied machine learning teams
- Product developers
