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) for Government
Provisioning Virtual Machines in the Cloud for Government
Scaling Considerations (CPUs, GPUs, and FPGAs) for Government
Navigating Azure Machine Learning Studio for Government
Preparing Data for Government Use Cases
Building a Model for Government Applications
Training and Testing a Model for Government Requirements
Registering a Trained Model for Government Operations
Building a Model Image for Government Deployment
Deploying a Model for Government Services
Monitoring a Model in Production for Government Efficiency
Troubleshooting for Government Users
Summary and Conclusion for Government Applications
Requirements
- An understanding of machine learning concepts.
- Familiarity with cloud computing principles.
- A general knowledge of containerization (Docker) and orchestration (Kubernetes).
- Experience in Python or R programming is beneficial.
- Proficiency in working with command-line interfaces.
Audience
- Data science engineers for government
- DevOps engineers interested 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
