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Duration 14 hours
Course Outline
Introduction to Devstral and Mistral Models
- Overview of Mistral’s open-source model lineup
- Apache-2.0 licensing terms and enterprise adoption strategies
- The role of Devstral in coding and agentic workflows
Self-Hosting Mistral and Devstral Models
- Preparing environments and selecting infrastructure
- Containerization and deployment using Docker and Kubernetes
- Considerations for scaling in production environments
Fine-Tuning Techniques
- Comparing supervised fine-tuning with parameter-efficient tuning
- Methods for dataset preparation and cleansing
- Examples of domain-specific customization
Model Operations 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 contexts
- Adhering to compliance frameworks and responsible AI practices
Monitoring and Observability
- Tracking model drift and accuracy degradation
- Instrumentation techniques for inference performance
- Defining alerting and response workflows
Case Studies and Best Practices
- Industry use cases showcasing Mistral and Devstral adoption
- Balancing cost, performance, and control
- Key lessons from open-source Model Operations
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows
- Proficiency with Python-based ML frameworks
- Knowledge of containerization and deployment environments
Target Audience
- ML Engineers
- Data Platform Teams
- Research Engineers
