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Duration 14 hours
Course Outline
Introduction to Devstral and Mistral Models
- Overview of Mistral’s open-source model suite
- 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
- Containerization and deployment using Docker/Kubernetes
- Scalability considerations for production workloads
Fine-Tuning Techniques
- Supervised fine-tuning versus parameter-efficient tuning
- Dataset preparation and data cleaning processes
- Examples of domain-specific customization
Model Ops and Versioning
- Best practices for managing the model lifecycle
- Model versioning and rollback strategies
- Integrating CI/CD pipelines for ML models
Governance and Compliance
- Security considerations for open-source deployments
- Ensuring monitoring and auditability in enterprise settings
- Compliance frameworks and responsible AI practices
Monitoring and Observability
- Tracking model drift and accuracy degradation
- Instrumenting for inference performance
- Establishing alerting and incident response workflows
Case Studies and Best Practices
- Industry use cases demonstrating Mistral and Devstral adoption
- Balancing cost, performance, and control
- Key lessons from open-source Model Ops
Summary and Next Steps
Requirements
- Proficiency in machine learning workflows
- Practical experience with Python-based ML frameworks
- Knowledge of containerization and deployment environments
Target Audience
- ML Engineers
- Data Platform Teams
- Research Engineers
