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 strategies.
- The role of Devstral in coding and agentic workflows.
Self-Hosting Mistral and Devstral Models
- Environment preparation and infrastructure selection.
- Containerization and deployment using Docker and Kubernetes.
- Scaling considerations for production use.
Fine-Tuning Techniques
- Supervised fine-tuning versus parameter-efficient tuning.
- Dataset preparation and cleaning processes.
- Examples of domain-specific customization.
Model Ops and Versioning
- Best practices for model lifecycle management.
- Model versioning and rollback strategies.
- CI/CD pipelines for machine learning 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
- Understanding of machine learning workflows.
- Experience with Python-based machine learning frameworks.
- Familiarity with containerization and deployment environments.
Audience
- Machine Learning (ML) engineers.
- Data platform teams.
- Research engineers.
14 Hours
