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Course Outline
Introduction to Devstral and Mistral Models for Government
- Overview of Mistral’s Open-Source Models
- Apache-2.0 Licensing and Enterprise Adoption for Government
- Devstral’s Role in Coding and Agentic Workflows for Government
Self-Hosting Mistral and Devstral Models for Government
- Environment Preparation and Infrastructure Choices for Government
- Containerization and Deployment with Docker/Kubernetes for Government
- Scaling Considerations for Production Use in Government Settings
Fine-Tuning Techniques for Government Models
- Supervised Fine-Tuning vs. Parameter-Efficient Tuning for Government Applications
- Dataset Preparation and Cleaning for Government Data Sources
- Domain-Specific Customization Examples for Government Use Cases
Model Ops and Versioning for Government
- Best Practices for Model Lifecycle Management in Government Agencies
- Model Versioning and Rollback Strategies for Government Systems
- CI/CD Pipelines for Machine Learning Models in Government Workflows
Governance and Compliance for Government
- Security Considerations for Open-Source Deployment in Government Environments
- Monitoring and Auditability in Enterprise Contexts for Government
- Compliance Frameworks and Responsible AI Practices for Government Agencies
Monitoring and Observability for Government Models
- Tracking Model Drift and Accuracy Degradation for Government Systems
- Instrumentation for Inference Performance in Government Applications
- Alerting and Response Workflows for Government Operations
Case Studies and Best Practices for Government
- Industry Use Cases of Mistral and Devstral Adoption in Government Agencies
- Balancing Cost, Performance, and Control in Government Settings
- Lessons Learned from Open-Source Model Ops in Government Operations
Summary and Next Steps for Government
Requirements
- An understanding of machine learning workflows for government applications
- Experience with Python-based ML frameworks for government projects
- Familiarity with containerization and deployment environments for government systems
Audience
- Machine Learning Engineers
- Data Platform Teams
- Research Engineers
14 Hours
