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Course Outline
Overview of Devstral and Mistral Models
- Summary of Mistral’s open-source model portfolio
- Application of Apache-2.0 licensing to support enterprise initiatives for government
- Role of Devstral in facilitating code generation and agentic workflows
Deploying Mistral and Devstral Models on Premises
- Preparation of operational environments and infrastructure selection
- Implementation of containerization and deployment using Docker or Kubernetes
- Scalability requirements for mission-critical production systems
Fine-Tuning Methodologies
- Comparison of supervised fine-tuning and parameter-efficient techniques
- Preparation and sanitization of training datasets
- Examples of customization for specific domain applications
Model Operations and Version Control
- Standards for comprehensive model lifecycle management
- Strategies for version control and rollback procedures
- Implementation of CI/CD pipelines for machine learning models for government use
Governance and Regulatory Compliance
- Security protocols for open-source deployments
- Maintaining monitoring capabilities and audit trails in enterprise settings
- Adherence to compliance frameworks and responsible AI standards
Performance Monitoring and Observability
- Detection of model drift and analysis of accuracy degradation
- Implementation of instrumentation for inference performance metrics
- Establishment of alerting mechanisms and incident response workflows
Case Studies and Operational Best Practices
- Review of industry adoption cases involving Mistral and Devstral
- Evaluation of trade-offs among cost, performance, and operational control
- Key insights derived from open-source Model Ops experiences
Summary and Future Directions
Requirements
- Comprehension of machine learning operational processes
- Practical application of Python-based machine learning tools
- Knowledge of containerization standards and deployment configurations
Target Audience
- Machine Learning engineers
- Data platform personnel
- Research scientists
14 Hours
