Open-Source Model Ops: Self-Hosting, Fine-Tuning and Governance with Devstral & Mistral Models Training Course

Introduction to Devstral and Mistral Models

• Overview of Mistral’s open-source model portfolio
• Apache-2.0 licensing and its role in enterprise adoption
• Devstral’s application in coding and agentic workflows

Self-Hosting Mistral and Devstral Models

• Environment preparation and infrastructure decision-making
• Containerization and deployment strategies using Docker/Kubernetes
• Scaling considerations for production workloads

Fine-Tuning Techniques

• Comparing supervised fine-tuning with parameter-efficient tuning
• Dataset preparation and data cleaning processes
• Examples of domain-specific customization

Model Ops and Versioning

• Best practices for managing the model lifecycle
• Strategies for model versioning and rollback
• Integrating CI/CD pipelines for ML models

Governance and Compliance

• Security implications of open-source deployments
• Ensuring monitoring and auditability in enterprise settings
• Adhering to compliance frameworks and responsible AI practices

Monitoring and Observability

• Tracking model drift and potential accuracy degradation
• Instrumenting inference performance metrics
• Designing alerting and response workflows

Case Studies and Best Practices

• Industry use cases for adopting Mistral and Devstral
• Balancing cost efficiency, performance, and control
• Key takeaways from open-source Model Ops experiences

Summary and Next Steps