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Course Outline
Introduction to Production Deployment for Government
- Key challenges in deploying fine-tuned models within government environments
- Differences between development and production environments in the public sector
- Tools and platforms for model deployment that align with government standards
Preparing Models for Deployment
- Exporting models in standard formats (ONNX, TensorFlow SavedModel, etc.) to ensure interoperability
- Optimizing models for latency and throughput to meet public sector performance requirements
- Testing models on edge cases and real-world data to ensure reliability and accuracy
Containerization for Model Deployment
- Introduction to Docker for government use
- Creating Docker images for ML models that adhere to government security protocols
- Best practices for container security and efficiency in the public sector
Scaling Deployments with Kubernetes
- Introduction to Kubernetes for AI workloads in government contexts
- Setting up Kubernetes clusters for model hosting that comply with federal IT standards
- Load balancing and horizontal scaling to ensure robust performance and scalability
Model Monitoring and Maintenance
- Implementing monitoring with Prometheus and Grafana to support government oversight
- Automated logging for error tracking and performance to enhance accountability and transparency
- Retraining pipelines for model drift and updates to maintain accuracy and relevance
Ensuring Security in Production
- Securing APIs for model inference to protect sensitive government data
- Authentication and authorization mechanisms that align with federal security guidelines
- Addressing data privacy concerns to ensure compliance with public sector regulations
Case Studies and Hands-On Labs
- Deploying a sentiment analysis model for government use
- Scaling a machine translation service to support multilingual communication in the public sector
- Implementing monitoring for image classification models to enhance security and efficiency in government operations
Summary and Next Steps
Requirements
- Strong understanding of machine learning workflows for government applications
- Experience with fine-tuning machine learning models
- Familiarity with DevOps or MLOps principles for efficient and secure operations
Audience
- DevOps engineers for government agencies
- MLOps practitioners in the public sector
- AI deployment specialists supporting government initiatives
21 Hours
Testimonials (1)
There were many practical exercises supervised and assisted by the trainer
