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 ecosystem
• Apache-2.0 licensing and strategies for enterprise adoption
• The role of Devstral in coding and agentic workflows

Self-Hosting Mistral and Devstral Models

• Environment preparation and infrastructure selection
• Containerization and deployment using Docker and Kubernetes
• Scaling considerations for production workloads

Fine-Tuning Techniques

• Supervised fine-tuning versus 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
• Model versioning and rollback strategies
• Implementing CI/CD pipelines for ML models

Governance and Compliance

• Security considerations for open-source deployments
• Monitoring and auditability in enterprise settings
• Compliance frameworks and responsible AI practices

Monitoring and Observability

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

Case Studies and Best Practices

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

Summary and Next Steps