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Course Outline
Introduction to Devstral and Mistral Models
- Overview of Mistral’s open-source models.
- Apache-2.0 licensing and enterprise adoption.
- The role of Devstral in coding and agentic workflows.
Self-Hosting Mistral and Devstral Models
- Environment preparation and infrastructure choices.
- Containerization and deployment using Docker/Kubernetes.
- Scaling considerations for production use.
Fine-Tuning Techniques
- Supervised fine-tuning vs. parameter-efficient tuning.
- Dataset preparation and cleaning.
- Domain-specific customization examples.
Model Ops and Versioning
- Best practices for model lifecycle management.
- Model versioning and rollback strategies.
- CI/CD pipelines for ML models.
Governance and Compliance
- Security considerations for open-source deployment.
- Monitoring and auditability in enterprise contexts.
- Compliance frameworks and responsible AI practices.
Monitoring and Observability
- Tracking model drift and accuracy degradation.
- Instrumentation for inference performance.
- Alerting and response workflows.
Case Studies and Best Practices
- Industry use cases of Mistral and Devstral adoption.
- Balancing cost, performance, and control.
- Lessons learned from open-source Model Ops.
Summary and Next Steps
Requirements
- Understanding of machine learning workflows.
- Experience with Python-based machine learning frameworks.
- Familiarity with containerization and deployment environments.
Audience
- ML engineers.
- Data platform teams.
- Research engineers.
14 Hours
