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Designing Machine Learning Solutions on Microsoft Azure

via Pluralsight


This course will cover how to leverage Azure Machine Learning for a successful data science initiative across the key components of workflow, data pipeline, and infrastructure.

When working on data science initiatives it can be challenging to gain actionable insights from your data set. In this course, Designing Machine Learning Solutions on Microsoft Azure, you will learn how to leverage Azure's Machine Learning capabilities to greatly increase the chance of success for your data science project. First, you will engage in team workflow and how Microsoft's Team Data Science Process (TDSP) enables best practices across disciplines. Next, you will discover the workflow of the Azure Machine Learning Service and how it can be leveraged on your project. After this, you will review how to create a pipeline for your data preparation, model training, and model registration. Finally, you will explore the infrastructure approaches that can be leveraged for machine learning and how those approaches are supported on Azure. When you are finished with this course, you will possess the skills that will be needed to start a data science project on Azure and the tools that will increase your ability to gain those actionable insights.

  • Course Overview
  • Understanding the Azure Machine Learning Workflow
  • Working with the Azure Machine Learning Workflow
  • Understanding Data Ingestion Strategies
  • Understanding Azure Machine Learning Infrastructure

Taught by

David Tucker

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