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Duration 14 hours
Course Outline
Introduction to Devstral and Mistral Models
- An overview of Mistral’s open-source model portfolio
- Apache-2.0 licensing terms and their implications for enterprise adoption
- The role of Devstral in coding tasks and agentic workflows
Self-Hosting Mistral and Devstral Models
- Preparing environments and selecting appropriate infrastructure
- Implementing containerization and deployment using Docker and Kubernetes
- Addressing scaling requirements for production environments
Fine-Tuning Techniques
- Comparing supervised fine-tuning with parameter-efficient tuning methods
- Strategies for dataset preparation and data cleaning
- Examples of customization for specific domains
Model Operations and Versioning
- Best practices for managing the model lifecycle
- Implementing model versioning and effective rollback strategies
- Integrating CI/CD pipelines for machine learning models
Governance and Compliance
- Security considerations specific to open-source deployment
- Establishing monitoring and auditability within enterprise contexts
- Adhering to compliance frameworks and responsible AI practices
Monitoring and Observability
- Tracking model drift and detecting accuracy degradation
- Instrumenting systems to measure inference performance
- Defining alerting mechanisms and response workflows
Case Studies and Best Practices
- Real-world industry examples of Mistral and Devstral adoption
- Balancing factors such as cost, performance, and operational control
- Key lessons learned from open-source Model Operations
Summary and Recommended Next Steps
Requirements
- A solid grasp of machine learning workflows
- Practical experience with Python-based machine learning frameworks
- Knowledge of containerization techniques and deployment environments
Intended Audience
- Machine Learning Engineers
- Data Platform Teams
- Research Engineers
