Open AI Agent Development with Mistral AI Training Course
Mistral AI is a powerful suite of open-source and enterprise-ready AI models designed for language, multimodal, and agentic applications.
This instructor-led, live training (available online or on-site) is targeted at intermediate to advanced professionals who aim to build, deploy, and manage AI agents using Mistral’s Medium 3, Le Chat Enterprise, and Devstral models.
By the end of this training, participants will be able to:
- Comprehend the architecture and capabilities of Mistral Medium 3, Le Chat Enterprise, and Devstral.
- Design and implement AI agents utilizing Mistral models for enterprise and developer scenarios.
- Integrate coding systems, connectors, and enterprise data into agent workflows.
- Optimize performance, cost, and compliance for agents powered by Mistral.
Format of the Course
- Interactive lectures and discussions.
- Numerous exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction to Mistral AI Ecosystem
- Overview of Mistral models (Medium 3, Le Chat Enterprise, Devstral)
- Positioning in the agentic AI ecosystem
- Key features and differentiators
Agent Design Principles
- What makes an AI agent
- Defining agent roles, memory, and tools
- Enterprise vs developer-centric agents
Hands-On with Mistral Medium 3
- Model setup and configuration
- Inference tuning and optimization
- Multimodal and coding workflows
Building with Devstral
- Code-first agent design
- Integrating Devstral for code understanding
- Engineering assistant best practices
Le Chat Enterprise Integration
- Deploying Le Chat for enterprise agents
- RBAC, SSO, and compliance integration
- Connecting enterprise apps and data stores
End-to-End Agent Workflows
- Combining Mistral Medium 3, Devstral, and Le Chat
- Building multi-tool workflows (connectors, APIs, data sources)
- Grounding and RAG patterns
Deployment and Governance
- Self-hosting vs API deployment
- Monitoring, logging, and observability
- Cost, performance, and compliance considerations
Summary and Next Steps
Requirements
- An understanding of Python programming
- Experience with machine learning workflows
- Familiarity with APIs and model integration
Audience
- AI engineers
- Solution architects
- Applied ML teams
- Product developers
Open Training Courses require 5+ participants.
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