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Duration 14 hours
Course Outline
Introduction to Devstral and Mistral Models
- Overview of Mistral’s open-source model landscape
- Apache-2.0 licensing and its role in enterprise adoption
- Devstral’s application in coding and agentic workflows
Self-Hosting Mistral and Devstral Models
- Preparing environments and selecting infrastructure options
- Containerization and deployment strategies using Docker/Kubernetes
- Addressing scaling considerations for production workloads
Fine-Tuning Techniques
- Comparing supervised fine-tuning with parameter-efficient tuning
- Effective dataset preparation and cleaning processes
- Examples of domain-specific customization
Model Ops and Versioning
- Best practices for managing the model lifecycle
- Strategies for model versioning and rollbacks
- Integrating CI/CD pipelines for ML models
Governance and Compliance
- Security considerations for deploying open-source models
- Ensuring monitoring and auditability in enterprise contexts
- Adhering to compliance frameworks and responsible AI practices
Monitoring and Observability
- Tracking model drift and accuracy degradation
- Instrumenting for optimal inference performance
- Defining alerting and response workflows
Case Studies and Best Practices
- Real-world industry use cases for Mistral and Devstral
- Balancing cost, performance, and control
- Key lessons learned from open-source Model Ops implementations
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows
- Hands-on experience with Python-based ML frameworks
- Knowledge of containerization and deployment environments
Target Audience
- ML engineers
- Data platform teams
- Research engineers
