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Course Outline
Introduction to Mistral Medium 3 for Government
- Model Architecture and Capabilities
- Comparison with Other Mistral Models
- Key Enterprise Applications for Government
Deployment Strategies for Government
- API-Based Deployment
- Self-Hosting with Docker and Kubernetes
- Hybrid and Multi-Cloud Considerations for Government
Performance Optimization for Government
- Batching and Parallelization Techniques
- Model Quantization and Acceleration
- Cost-Performance Tradeoffs for Government Operations
Multimodal Applications for Government
- Integrating Text and Image Processing for Government Services
- OCR and Document Intelligence for Government Records
- Cross-Modal Enterprise Workflows for Government Agencies
Security and Compliance for Government
- Data Residency and Privacy Considerations for Government Data
- Role-Based Access and Permissions for Government Users
- Auditability and Governance for Government Systems
Monitoring and Observability for Government
- Tracking Performance and Drift in Government Applications
- Logging and Metrics Pipelines for Government Operations
- Alerting and Troubleshooting for Government IT Teams
Scaling for Enterprise for Government
- Horizontal and Vertical Scaling Patterns for Government Systems
- Load Balancing and Redundancy for Government Services
- Disaster Recovery Strategies for Government Operations
Summary and Next Steps for Government
Requirements
- Proficiency in Python or a comparable programming language
- Experience in deploying machine learning models
- Familiarity with cloud or containerized environments
Audience for Government
- AI/ML engineers
- Platform architects
- MLOps teams
14 Hours