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Course Outline
Introduction to Mistral Medium 3 for Government
- Model Architecture and Capabilities
- Comparison with Other Mistral Models
- Key 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 Operations
Monitoring and Observability for Government
- Tracking Performance and Drift in Government Systems
- Logging and Metrics Pipelines for Government IT
- Alerting and Troubleshooting for Government Applications
Scaling for Enterprise for Government
- Horizontal and Vertical Scaling Patterns for Government Infrastructure
- 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 with the deployment of machine learning models
- Understanding of cloud-based or containerized environments
Audience
- AI/ML engineers for government
- Platform architects for government
- MLOps teams for government
14 Hours