Azure Data Scientist Associate: Machine Learning Orchestration & Deployment
Azure 2021
| Intermediate
- 11 videos | 1h 12m 57s
- Includes Assessment
- Earns a Badge
Azure Machine Learning Studio provides DevOps support in the form of orchestrating machine learning pipelines. In this course, you'll learn to create, publish, and schedule machine learning pipelines. First, you'll examine Azure Machine Learning pipelines and how they are used to build, optimize, and manage machine learning workflows. Next, you'll explore how to use the Azure Machine Learning SDK to create and run machine learning pipelines. You'll learn how to use a pipeline to import, transform, and move data between steps, as well as how to publish and track pipelines and use triggers to schedule a machine learning pipeline based on some event. Finally, you'll learn techniques for troubleshooting and debugging machine learning pipelines This course is one in a collection that prepares learners for the Designing and Implementing a Data Science Solution on Azure (DP-100) exam.
WHAT YOU WILL LEARN
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discover the key concepts covered in this course
describe Azure Machine Learning pipelines and how they are used to build, optimize, and manage machine learning workflows
use Azure Machine Learning pipelines to import, transform, and move data between steps
use the Azure Machine Learning SDK to create and run machine learning pipelines
publish and track Machine Learning pipelines and share them with others
schedule a Machine Learning pipeline based on an elapsed time or file system changes -
describe techniques for troubleshooting and debugging Machine Learning pipelines
deploy a model as a real-time service to different compute targets
consume a real-time service that can be used to predict labels
troubleshoot a failed deployment using various techniques
summarize the key concepts covered in this course
IN THIS COURSE
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1m 25s
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8m 21s
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3. Moving Data Using Machine Learning Pipelines10m 31s
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4. Creating Machine Learning Pipelines7m 48s
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5. Publishing Machine Learning Pipelines7m 1s
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6. Triggering Machine Learning Pipelines6m 38s
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7. Troubleshooting Machine Learning Pipelines7m 11s
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8. Deploying Real-time Services8m 46s
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9. Consuming Real-time Services6m 33s
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10. Troubleshooting Service Deployments8m 3s
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11. Course Summary40s
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