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Duration 14 hours
Course Outline
Introduction to Devstral and Mistral Models
- Overview of Mistral’s open-source model architecture
- Apache-2.0 licensing and enterprise adoption strategies
- The role of Devstral in coding and agentic workflows
Self-Hosting Mistral and Devstral Models
- Preparing environments and selecting infrastructure options
- Containerization and deployment using Docker/Kubernetes
- Considerations for scaling production workloads
Fine-Tuning Techniques
- Comparing supervised fine-tuning with parameter-efficient tuning
- Dataset preparation and data cleaning best practices
- Examples of domain-specific model customization
Model Ops and Versioning
- Best practices for managing the model lifecycle
- Strategies for model versioning and rollback procedures
- Integrating CI/CD pipelines for ML models
Governance and Compliance
- Security considerations for open-source deployments
- Ensuring monitoring and auditability in enterprise contexts
- Aligning with compliance frameworks and responsible AI principles
Monitoring and Observability
- Tracking model drift and accuracy degradation over time
- Instrumenting systems for inference performance analysis
- Establishing alerting and incident response workflows
Case Studies and Best Practices
- Industry use cases demonstrating Mistral and Devstral adoption
- Balancing cost efficiency, performance, and control
- Key lessons learned from open-source Model Ops practices
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows.
- Proficiency with Python-based ML frameworks.
- Knowledge of containerization and deployment environments.
Intended Audience
- ML Engineers
- Data Platform Teams
- Research Engineers
