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

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