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Duration 14 hours
Course Outline
Introduction to Devstral and Mistral Models
- Overview of Mistral’s open-source model lineup
- Apache-2.0 licensing and its impact on enterprise adoption
- The role of Devstral in coding and agentic workflows
Self-Hosting Mistral and Devstral Models
- Preparing the environment and selecting infrastructure
- Containerizing and deploying using Docker or Kubernetes
- Scaling strategies for production workloads
Fine-Tuning Methodologies
- Comparing supervised fine-tuning with parameter-efficient tuning
- Preparing and cleaning datasets
- Examples of domain-specific customization
Model Operations and Versioning
- Best practices for managing the model lifecycle
- Strategies for model versioning and rollback
- Implementing CI/CD pipelines for ML models
Governance and Compliance
- Security considerations when deploying open-source models
- Ensuring monitoring and auditability in enterprise settings
- Aligning with compliance frameworks and responsible AI practices
Monitoring and Observability
- Tracking model drift and performance degradation
- Instrumenting inference performance
- Setting up alerting and response workflows
Case Studies and Best Practices
- Industry use cases for adopting Mistral and Devstral
- Balancing cost, performance, and control
- Key lessons from open-source Model Ops experiences
Summary and Next Steps
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
