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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 foundational understanding of machine learning concepts.
- Knowledge of cloud computing principles.
- General familiarity with containers (Docker) and orchestration tools (Kubernetes).
- Experience with Python or R programming is advantageous.
- Proficiency in using the command line.
Audience
- Data science engineers.
- DevOps engineers interested in deploying machine learning models.
- Infrastructure engineers interested in machine learning model deployment.
- Software engineers seeking to automate the integration and deployment of machine learning capabilities within their applications.
21 Hours
Testimonials (2)
The details and the presentation style.
Cristian Mititean - Accenture Industrial SS
Course - Azure Machine Learning (AML)
The Exercises
