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Duration 14 hours
Course Outline
Introduction to Devstral and Mistral Models
- An overview of Mistral’s open-source model offerings
- Details on 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 environments and selecting infrastructure options
- Containerization and deployment strategies using Docker and Kubernetes
- Key scaling considerations for production workloads
Fine-Tuning Techniques
- Comparing supervised fine-tuning with parameter-efficient tuning
- Methods for dataset preparation and data cleaning
- Examples of domain-specific customizations
Model Ops and Versioning
- Best practices for managing the model lifecycle
- Strategies for model versioning and rollback mechanisms
- Integrating CI/CD pipelines for ML models
Governance and Compliance
- Security implications of open-source deployments
- Ensuring monitoring and auditability in enterprise contexts
- Adhering to compliance frameworks and responsible AI standards
Monitoring and Observability
- Methods for tracking model drift and accuracy degradation
- Instrumenting systems for inference performance
- Establishing alerting and incident response workflows
Case Studies and Best Practices
- Real-world industry examples of Mistral and Devstral adoption
- Balancing cost, performance, and operational control
- Key takeaways from open-source Model Ops experiences
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
