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Duration 14 hours
Course Outline
Introduction to Devstral and Mistral Models
- An overview of Mistral's open-source model ecosystem
- Exploring Apache-2.0 licensing and its relevance to enterprise adoption
- The specific role of Devstral in coding and agentic workflows
Self-Hosting Mistral and Devstral Models
- Preparing environments and selecting appropriate infrastructure
- Managing containerization and deployment via Docker and Kubernetes
- Addressing scaling requirements for production workloads
Fine-Tuning Methodologies
- Comparing supervised fine-tuning with parameter-efficient tuning
- Strategies for dataset preparation and cleaning
- Examples of customization for specific domains
Model Ops and Version Control
- Best practices for managing the model lifecycle
- Implementing versioning and rollback strategies
- Integrating CI/CD pipelines for ML models
Governance and Compliance
- Security protocols for open-source deployments
- Ensuring monitoring and auditability in enterprise settings
- Adhering to compliance frameworks and responsible AI principles
Monitoring and Observability
- Detecting model drift and accuracy degradation
- Instrumenting systems to track inference performance
- Establishing alerting mechanisms and response workflows
Case Studies and Industry Best Practices
- Real-world applications of Mistral and Devstral in various industries
- Striking a balance between cost, performance, and control
- Key takeaways from open-source Model Ops practices
Conclusion and Path Forward
Requirements
- A solid grasp of machine learning workflows
- Hands-on experience with Python-based ML frameworks
- Working knowledge of containerization and deployment environments
Target Audience
- Machine Learning Engineers
- Data Platform Teams
- Research Engineers
