Course Outline
Introduction to Containerization for AI & ML for Government
- Core Concepts of Containerization
- Why Containers Are Ideal for ML Workloads in the Public Sector
- Key Differences Between Containers and Virtual Machines for Government
Working with Docker Images and Containers for Government
- Understanding Images, Layers, and Registries for Government Use
- Managing Containers for ML Experimentation in Public Sector Projects
- Using the Docker CLI Efficiently for Government Operations
Packaging ML Environments for Government Applications
- Preparing ML Codebases for Containerization for Government Use
- Managing Python Environments and Dependencies for Government Projects
- Integrating CUDA and GPU Support for Government AI Initiatives
Building Dockerfiles for Machine Learning for Government
- Structuring Dockerfiles for ML Projects in the Public Sector
- Best Practices for Performance and Maintainability for Government Applications
- Using Multi-Stage Builds for Efficient Government Deployments
Containerizing ML Models and Pipelines for Government
- Packaging Trained Models into Containers for Government Use
- Managing Data and Storage Strategies for Government ML Projects
- Deploying Reproducible End-to-End Workflows for Government Operations
Running Containerized ML Services for Government
- Exposing API Endpoints for Model Inference in Government Systems
- Scaling Services with Docker Compose for Government Needs
- Monitoring Runtime Behavior for Government Compliance and Security
Security and Compliance Considerations for Government ML Projects
- Ensuring Secure Container Configurations for Government Use
- Managing Access and Credentials for Government Systems
- Handling Confidential ML Assets in the Public Sector
Deploying to Production Environments for Government
- Publishing Images to Container Registries for Government Use
- Deploying Containers in On-Prem or Cloud Setups for Government Operations
- Versioning and Updating Production Services for Government Applications
Summary and Next Steps for Government ML Initiatives
Requirements
- A comprehensive understanding of machine learning workflows for government applications
- Practical experience with Python or similar programming languages
- Proficiency in basic Linux command-line operations
Audience
- Machine learning engineers deploying models to production environments for government use
- Data scientists managing reproducible experiment environments within public sector projects
- Artificial intelligence developers building scalable, containerized applications for government initiatives
Testimonials (5)
OC is new to us and we learnt alot and the labs were excellent
sharkey dollie
Course - OpenShift 4 for Administrators
Very informative and to the point. Hands on pratice
Gil Matias - FINEOS
Course - Introduction to Docker
Labs and technical discussions.
Dinesh Panchal - AXA XL
Course - Advanced Docker
It gave a good grounding for Docker and Kubernetes.
Stephen Dowdeswell - Global Knowledge Networks UK
Course - Docker (introducing Kubernetes)
I mostly enjoyed the knowledge of the trainer.
