Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Devstral and Mistral Models
- Overview of Mistral’s open-source models
- Apache-2.0 licensing and enterprise adoption
- Devstral’s role in coding and agentic workflows
Self-Hosting Mistral and Devstral Models
- Environment preparation and infrastructure choices
- Containerization and deployment with Docker/Kubernetes
- Scaling considerations for production use
Fine-Tuning Techniques
- Supervised fine-tuning vs parameter-efficient tuning
- Dataset preparation and cleaning
- Domain-specific customization examples
Model Ops and Versioning
- Best practices for model lifecycle management
- Model versioning and rollback strategies
- CI/CD pipelines for ML models
Governance and Compliance
- Security considerations for open-source deployment
- Monitoring and auditability in enterprise contexts
- Compliance frameworks and responsible AI practices
Monitoring and Observability
- Tracking model drift and accuracy degradation
- Instrumentation for inference performance
- Alerting and response workflows
Case Studies and Best Practices
- Industry use cases of Mistral and Devstral adoption
- Balancing cost, performance, and control
- Lessons learned from open-source Model Ops
Summary and Next Steps
Requirements
- An understanding of machine learning workflows
- Experience with Python-based ML frameworks
- Familiarity with containerization and deployment environments
Audience
- ML engineers
- Data platform teams
- Research engineers
14 Hours
