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Designing and Implementing a Data Science Solution on Azure (DP-100T01)

Gain the necessary knowledge about how to use Azure services to develop, train, and deploy, machine learning solutions.
Microsoft Partner

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  • Duration: 4 days
  • Regular price: $2,595.00
  • Preferential price: $2,205.76tip icon

Course outline

Reference : @Microsoft DP-100T01-A

Duration : 4 days

© AFI par Edgenda inc.

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

Audience

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

Prerequisites

Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques.

Specifically:

  • Creating cloud resources in Microsoft Azure.

  • Using Python to explore and visualize data.

  • Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.

  • Working with containers

To gain these prerequisite skills, take the following free online training before attending the course:

If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.

Contents

  • Design a data ingestion strategy for machine learning projects
  • Design a machine learning model training solution

  • Design a model deployment solution

  • Explore Azure Machine Learning workspace resources and assets

  • Explore developer tools for workspace interaction

  • Make data available in Azure Machine Learning

  • Work with compute targets in Azure Machine Learning

  • Run pipelines in Azure Machine Learning

  • Perform hyperparameter tuning with Azure Machine Learning

  • Deploy a model to a managed online endpoint

  • Deploy a model to a batch endpoint