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Duration 14 hours
Course Outline
Introduction to Devstral and Mistral Models
- Overview of Mistral’s open-source model offerings
- 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 selection
- Containerization and deployment using Docker and Kubernetes
- Scaling considerations for production usage
Fine-Tuning Techniques
- Supervised fine-tuning versus parameter-efficient tuning
- Dataset preparation and cleaning processes
- Examples of domain-specific customization
Model Ops and Versioning
- Best practices for managing the model lifecycle
- Model versioning and rollback strategies
- Implementing CI/CD pipelines for ML models
Governance and Compliance
- Security considerations for open-source deployments
- 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
- Key lessons learned from open-source Model Ops
Summary and Next Steps
Requirements
- A solid understanding of machine learning workflows
- Experience with Python-based ML frameworks
- Familiarity with containerization and deployment environments
Target Audience
- ML engineers
- Data platform teams
- Research engineers
