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
- An overview of Mistral’s open-source model lineup
- Apache-2.0 licensing implications for enterprise adoption
- The specific role of Devstral in coding and agentic workflows
Self-Hosting Mistral and Devstral Models
- Preparing the environment and selecting infrastructure options
- Containerization and deployment strategies using Docker/Kubernetes
- Scaling strategies for production environments
Fine-Tuning Techniques
- Comparing supervised fine-tuning with parameter-efficient methods
- Preparing and cleaning datasets for training
- Examples of domain-specific customization
Model Operations and Versioning
- Best practices for managing the model lifecycle
- Versioning schemes and rollback strategies
- Integrating ML models into CI/CD pipelines
Governance and Compliance
- Security considerations for 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 inference performance metrics
- Setting up alerting and response workflows
Case Studies and Best Practices
- Industry examples of adopting Mistral and Devstral
- Striking a balance between cost, performance, and control
- Key lessons learned from open-source Model Operations
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows
- Proficiency with Python-based ML frameworks
- Working knowledge of containerization and deployment environments
Target Audience
- ML engineers
- Data platform teams
- Research engineers
