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

Introduction to Devstral and Mistral Models

• An overview of Mistral's open-source model ecosystem
• Apache-2.0 licensing details and enterprise adoption strategies
• The role of Devstral in coding and agentic workflows

Self-Hosting Mistral and Devstral Models

• Preparing environments and selecting infrastructure options
• Containerization and deployment using Docker/Kubernetes
• Key scaling considerations for production workloads

Fine-Tuning Techniques

• Comparing supervised fine-tuning with parameter-efficient tuning
• Methods for dataset preparation and cleaning
• 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 considerations for 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 assessing accuracy degradation
• Instrumenting inference performance
• Designing alerting and response workflows

Case Studies and Best Practices

• Industry use cases illustrating Mistral and Devstral adoption
• Balancing cost, performance, and control
• Key lessons learned from open-source Model Ops

Summary and Next Steps