IT Professional Curricula     Internet and Network Technologies Solution Area     Cloud Computing     DP-100: Designing and Implementing a Data Science Solution on Azure

---

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.

---

---

Objectives

DP-100 - Azure Data Scientist Associate: Machine Learning Orchestration & Deployment 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