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Duration 14 hours
Course Outline
Introduction to Devstral and Mistral Models
- Overview of Mistral’s open-source model ecosystem
- Implications of Apache-2.0 licensing for enterprise adoption
- The role of Devstral in coding and agentic workflows
Self-Hosting Mistral and Devstral Models
- Preparing environments and selecting infrastructure
- Containerization and deployment via Docker and Kubernetes
- Scaling strategies for production-grade usage
Fine-Tuning Techniques
- Comparing supervised fine-tuning with parameter-efficient tuning
- Dataset preparation and data cleansing
- Examples of domain-specific customization
Model Ops and Versioning
- Best practices for managing the model lifecycle
- Versioning controls and rollback strategies
- Integrating CI/CD pipelines for ML models
Governance and Compliance
- Security considerations for open-source deployments
- Ensuring monitoring and auditability in enterprise settings
- Aligning with compliance frameworks and responsible AI practices
Monitoring and Observability
- Tracking model drift and accuracy degradation
- Instrumenting inference performance
- Establishing alerting and incident response workflows
Case Studies and Best Practices
- Industry use cases showcasing Mistral and Devstral adoption
- Balancing cost, performance, and operational control
- Key lessons learned from open-source Model Ops
Summary and Next Steps
Requirements
- Foundational knowledge of machine learning workflows
- Hands-on experience with Python-based ML frameworks
- Proficiency with containerization and deployment environments
Target Audience
- ML Engineers
- Data Platform Teams
- Research Engineers
