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Duration 14 hours
Course Outline
Introduction to Devstral and Mistral Models
- Overview of Mistral’s open-source model portfolio
- Apache-2.0 licensing implications and enterprise adoption strategies
- The role of Devstral in coding and agentic workflows
Self-Hosting Mistral and Devstral Models
- Environment setup and infrastructure decision-making
- Containerization and deployment using Docker and Kubernetes
- Scaling strategies for production-grade workloads
Fine-Tuning Methodologies
- Comparing supervised fine-tuning with parameter-efficient tuning
- Dataset curation, preparation, and cleansing
- Examples of domain-specific model customization
Model Operations and Versioning
- Best practices for managing the model lifecycle
- Version control and rollback protocols
- Integrating CI/CD pipelines for ML models
Governance and Regulatory Compliance
- Security protocols for open-source deployments
- Monitoring and audit trails within enterprise contexts
- Compliance frameworks and responsible AI standards
Monitoring and Observability
- Tracking model drift and accuracy decay
- Instrumenting inference performance metrics
- Designing alerting and incident response workflows
Case Studies and Industry Best Practices
- Real-world applications of Mistral and Devstral adoption
- Optimizing the balance between cost, performance, and control
- Key insights from open-source Model Ops practices
Conclusion and Future Recommendations
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
