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 using Docker/Kubernetes.
- Scaling considerations for production use.
Fine-Tuning Techniques
- Supervised fine-tuning vs. parameter-efficient tuning.
- Dataset preparation and cleaning.
- Examples of domain-specific customization.
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 MLOps.
Summary and Next Steps
Requirements
- A solid 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
