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
- Overview of Mistral’s open-source model ecosystem.
- Understanding Apache-2.0 licensing and its implications for enterprise adoption.
- The 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 and Kubernetes.
- Addressing scaling considerations for production workloads.
Fine-Tuning Techniques
- Comparing supervised fine-tuning with parameter-efficient tuning methods.
- Preparing and cleaning datasets for effective training.
- Examples of domain-specific customization.
Model Ops and Versioning
- Best practices for managing the model lifecycle.
- Strategies for model versioning and rollback procedures.
- Implementing CI/CD pipelines specifically for ML models.
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
- Tracking model drift and detecting accuracy degradation.
- Instrumenting systems for inference performance analysis.
- 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 implementations.
Summary and Next Steps
Requirements
- A solid grasp 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.
