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Duration 14 hours
Course Outline
Introduction to Devstral and Mistral Models
- Overview of Mistral’s open-source model offerings
- Apache-2.0 licensing implications and enterprise adoption
- Devstral’s contribution to coding and agentic workflows
Self-Hosting Mistral and Devstral Models
- Preparing environments and selecting infrastructure options
- Containerization and deployment strategies using Docker and Kubernetes
- Scaling considerations for production workloads
Fine-Tuning Techniques
- Comparing supervised fine-tuning and parameter-efficient tuning
- Preparing and cleaning datasets
- Examples of domain-specific customisation
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
- Monitoring and ensuring auditability in enterprise settings
- Aligning with compliance frameworks and responsible AI practices
Monitoring and Observability
- Tracking model drift and assessing accuracy degradation
- Instrumenting for optimal inference performance
- Defining alerting and response workflows
Case Studies and Best Practices
- Industry examples of Mistral and Devstral adoption
- Balancing cost, performance, and operational control
- Key lessons from open-source Model Ops
Summary and Next Steps
Requirements
- A solid understanding of machine learning workflows
- Experience with Python-based ML frameworks
- Familiarity with containerization and deployment environments
Target Audience
- ML engineers
- Data platform teams
- Research engineers
