Course Outline
Introduction
Overview of Azure Machine Learning (AML) Features and Architecture for Government
Overview of an End-to-End Workflow in AML (Azure Machine Learning Pipelines)
Provisioning Virtual Machines in the Cloud for Government Use
Scaling Considerations (CPUs, GPUs, and FPGAs) for Government Applications
Navigating Azure Machine Learning Studio for Efficient Government Operations
Preparing Data for Government Projects
Building a Model to Support Government Initiatives
Training and Testing a Model for Government Compliance
Registering a Trained Model for Government Use
Building a Model Image for Government Deployment
Deploying a Model in Government Environments
Monitoring a Model in Production for Government Operations
Troubleshooting for Government Applications
Summary and Conclusion
Requirements
- A clear understanding of machine learning concepts.
- Familiarity with cloud computing principles.
- General knowledge of containerization (Docker) and orchestration (Kubernetes).
- Experience with Python or R programming is beneficial.
- Proficiency in using command-line interfaces.
Audience for Government
- Data science engineers
- DevOps engineers with an interest in deploying machine learning models
- Infrastructure engineers focused on the deployment of machine learning models
- Software engineers seeking to automate the integration and deployment of machine learning features within their applications
Testimonials (2)
The details and the presentation style.
Cristian Mititean - Accenture Industrial SS
Course - Azure Machine Learning (AML)
The Exercises
