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Course Outline
Introduction to the Mistral AI Ecosystem
- Overview of Mistral models (Medium 3, Le Chat Enterprise, Devstral)
- Strategic positioning within the agentic AI landscape
- Core features and key differentiators
Principles of Agent Design
- Defining the characteristics of an AI agent
- Establishing agent roles, memory structures, and tool interactions
- Distinguishing between enterprise-focused and developer-centric agents
Practical Application of Mistral Medium 3
- Model configuration and initial setup
- Tuning inference parameters for optimal performance
- Implementing multimodal and coding-centric workflows
Development with Devstral
- Code-first approaches to agent architecture
- Utilizing Devstral for advanced code comprehension
- Best practices for engineering assistants
Integrating Le Chat Enterprise
- Deploying Le Chat for enterprise-level agent solutions
- Incorporating RBAC, SSO, and compliance frameworks
- Linking enterprise applications and data repositories
End-to-End Agent Workflows
- Synthesising Mistral Medium 3, Devstral, and Le Chat
- Constructing multi-tool workflows involving connectors, APIs, and data sources
- Implementing grounding and RAG patterns
Deployment and Governance Strategies
- Comparing self-hosting versus API-based deployment
- Establishing monitoring, logging, and observability standards
- Managing cost, performance, and compliance requirements
Conclusion and Future Directions
Requirements
- Solid proficiency in Python programming
- Practical experience with machine learning pipelines
- Working knowledge of APIs and model integration strategies
Target Audience
- AI Engineers
- Solution Architects
- Applied Machine Learning Teams
- Product Developers
14 Hours
