Deploying Fine-Tuned Models in Production Training Course

Introduction to Production Deployment for Government

• Key challenges in deploying fine-tuned models for government operations
• Differences between development and production environments for government use cases
• Tools and platforms for model deployment in a governmental context

Preparing Models for Deployment for Government

• Exporting models in standard formats (ONNX, TensorFlow SavedModel, etc.) for government systems
• Optimizing models for latency and throughput to meet public sector performance standards
• Testing models on edge cases and real-world data relevant to government operations

Containerization for Model Deployment in Government

• Introduction to Docker for government applications
• Creating Docker images for ML models used in governmental services
• Best practices for container security and efficiency in a public sector environment

Scaling Deployments with Kubernetes for Government

• Introduction to Kubernetes for AI workloads in government agencies
• Setting up Kubernetes clusters for model hosting in federal environments
• Load balancing and horizontal scaling for government applications

Model Monitoring and Maintenance for Government

• Implementing monitoring with Prometheus and Grafana for government systems
• Automated logging for error tracking and performance in public sector operations
• Retraining pipelines for model drift and updates to ensure ongoing accuracy for government use

Ensuring Security in Production for Government

• Securing APIs for model inference in government applications
• Authentication and authorization mechanisms for government systems
• Addressing data privacy concerns in public sector deployments

Case Studies and Hands-On Labs for Government

• Deploying a sentiment analysis model for government use
• Scaling a machine translation service for federal agencies
• Implementing monitoring for image classification models in governmental contexts

Summary and Next Steps for Government