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 offerings
• Apache-2.0 licensing and strategies for enterprise adoption
• Devstral’s capabilities in coding and agentic workflows

Self-Hosting Mistral and Devstral Models

• Preparing environments and selecting infrastructure
• Containerization and deployment using Docker/Kubernetes
• Addressing scaling requirements for production workloads

Fine-Tuning Techniques

• Comparing supervised fine-tuning with parameter-efficient tuning
• Preparing and cleaning datasets
• Examples of customization for specific domains

Model Operations and Versioning

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

Governance and Compliance

• Security considerations for deploying open-source models
• Ensuring monitoring and auditability in enterprise settings
• Aligning with compliance frameworks and responsible AI principles

Monitoring and Observability

• Tracking model drift and degradation in accuracy
• Instrumenting systems for inference performance
• Establishing alerting and response workflows

Case Studies and Best Practices

• Industry examples of adopting Mistral and Devstral
• Striking a balance between cost, performance, and control
• Key lessons from open-source Model Operations

Summary and Next Steps