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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 enterprise adoption strategies
- The role of Devstral in coding and agentic workflows
Self-Hosting Mistral and Devstral Models
- Infrastructure planning and environment preparation
- Deployment via Docker and Kubernetes
- Scalability considerations for production workloads
Fine-Tuning Techniques
- Comparing supervised fine-tuning with parameter-efficient tuning
- Dataset curation and cleaning processes
- Examples of domain-specific model customization
Model Ops and Versioning
- Best practices for managing the model lifecycle
- Versioning controls and rollback mechanisms
- Integrating CI/CD pipelines for ML models
Governance and Compliance
- Security best practices for open-source deployments
- Ensuring monitoring and auditability in enterprise settings
- Adhering to compliance frameworks and responsible AI principles
Monitoring and Observability
- Detecting model drift and performance degradation
- Instrumenting inference performance metrics
- Defining alerting and incident response workflows
Case Studies and Best Practices
- Real-world industry applications of Mistral and Devstral
- Optimizing the balance between cost, performance, and control
- Key takeaways 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
Target Audience
- ML engineers
- Data platform teams
- Research engineers
