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.
Duration 14 hours
Course Outline
Introduction to Devstral and Mistral Models
- Exploration of Mistral’s open-source model lineup
- Apache-2.0 licensing terms and enterprise adoption
- The role of Devstral in coding and agentic workflows
Self-Hosting Mistral and Devstral Models
- Preparation of environments and selection of infrastructure
- Containerization and deployment using Docker/Kubernetes
- Scaling strategies for production utilization
Fine-Tuning Methodologies
- Supervised fine-tuning versus parameter-efficient tuning
- Preparation and cleaning of datasets
- Examples of domain-specific customization
Model Ops and Version Control
- Best practices for managing the model lifecycle
- Model versioning and rollback protocols
- CI/CD pipelines specifically for ML models
Governance and Compliance
- Security aspects of open-source deployment
- Monitoring and auditability in enterprise settings
- Compliance frameworks and responsible AI practices
Monitoring and Observability
- Tracking model drift and degradation in accuracy
- Instrumentation for measuring inference performance
- Alerting mechanisms and response workflows
Case Studies and Industry Best Practices
- Real-world industry applications of Mistral and Devstral
- Balancing cost, performance, and operational control
- Key insights from open-source Model Ops experiences
Wrap-up and Future Steps
Requirements
- Foundational understanding 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
