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Duration 14 hours
Course Outline
Introduction to Devstral and Mistral Models
- Overview of Mistral's open-source model ecosystem
- Apache-2.0 licensing and strategies for enterprise adoption
- The role of Devstral in coding and agentic workflows
Self-Hosting Mistral and Devstral Models
- Environment setup and infrastructure selection
- Containerization and deployment strategies using Docker/Kubernetes
- Scaling considerations for production workloads
Fine-Tuning Methodologies
- Comparing supervised fine-tuning with parameter-efficient tuning
- Dataset curation and preprocessing
- Examples of domain-specific customization
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 audit capabilities in enterprise contexts
- Compliance frameworks and responsible AI practices
Monitoring and Observability
- Tracking model drift and accuracy decay
- Instrumentation for optimizing inference performance
- Designing alerting and incident response workflows
Case Studies and Industry Best Practices
- Real-world use cases of Mistral and Devstral adoption
- Balancing cost, performance, and control
- Key lessons from open-source Model Ops initiatives
Summary and Future Directions
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
