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Duration 14 hours
Course Outline
Introduction to Devstral and Mistral Models
- An overview of Mistral’s open-source model lineup
- Apache-2.0 licensing and its implications for enterprise adoption
- The role of Devstral in coding and agentic workflows
Self-Hosting Mistral and Devstral Models
- Preparing the environment and selecting infrastructure options
- Containerization and deployment using Docker and Kubernetes
- Scaling strategies for production workloads
Fine-Tuning Techniques
- Comparing supervised fine-tuning with parameter-efficient tuning
- Methods for dataset preparation and cleaning
- Examples of domain-specific customizations
Model Ops and Versioning
- Best practices for managing the model lifecycle
- Strategies for model versioning and rollback
- Integrating CI/CD pipelines for ML models
Governance and Compliance
- Security considerations in open-source deployments
- Ensuring monitoring and auditability in enterprise contexts
- Adhering to compliance frameworks and responsible AI practices
Monitoring and Observability
- Detecting model drift and accuracy degradation
- Instrumenting systems for inference performance
- Setting up alerting and response workflows
Case Studies and Best Practices
- Industry case studies on adopting Mistral and Devstral
- Balancing cost, performance, and 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
