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Course Outline
Introduction
Overview of Azure Machine Learning (AML) Features and Architecture
Overview of an End-to-End Workflow in AML (Azure Machine Learning Pipelines)
Provisioning Virtual Machines in the Cloud
Scaling Considerations (CPUs, GPUs, and FPGAs)
Navigating Azure Machine Learning Studio
Preparing Data
Building a Model
Training and Testing a Model
Registering a Trained Model
Building a Model Image
Deploying a Model
Monitoring a Model in Production
Troubleshooting
Summary and Conclusion
Requirements
- A solid understanding of machine learning concepts.
- Knowledge of cloud computing principles.
- General understanding of containers (Docker) and orchestration (Kubernetes).
- Experience with Python or R programming is advantageous.
- Experience using a command line interface.
Audience
- Data science engineers
- DevOps engineers interested in machine learning model deployment
- Infrastructure engineers interested in machine learning model deployment
- Software engineers aiming to automate the integration and deployment of machine learning features in their applications
21 Hours
Testimonials (2)
The details and the presentation style.
Cristian Mititean - Accenture Industrial SS
Course - Azure Machine Learning (AML)
The Exercises
