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 ecosystem.
- Apache-2.0 licensing implications and enterprise adoption.
- The role of Devstral in coding and agentic workflows.
Self-Hosting Mistral and Devstral Models
- Preparing environments and selecting infrastructure options.
- Containerization and deployment using Docker/Kubernetes.
- Scaling strategies for production workloads.
Fine-Tuning Techniques
- Comparing supervised fine-tuning with parameter-efficient tuning.
- Strategies for dataset preparation and cleaning.
- Examples of domain-specific customization.
Model Ops and Versioning
- Best practices for managing the model lifecycle.
- Implementing model versioning and rollback strategies.
- Integrating CI/CD pipelines for ML models.
Governance and Compliance
- Addressing security considerations in open-source deployments.
- Ensuring monitoring and auditability in enterprise contexts.
- Adhering to compliance frameworks and responsible AI practices.
Monitoring and Observability
- Tracking model drift and accuracy degradation.
- Instrumenting inference performance metrics.
- Establishing alerting and response workflows.
Case Studies and Best Practices
- Industry use cases demonstrating Mistral and Devstral adoption.
- Balancing cost, performance, and control.
- Key lessons learned from open-source Model Ops.
Summary and Next Steps
Requirements
- A solid understanding of machine learning workflows.
- Hands-on experience with Python-based ML frameworks.
- Knowledge of containerization and deployment environments.
Target Audience
- ML engineers.
- Data platform teams.
- Research engineers.
