Course DP-100T01-A: Designing and Implementing a Data Science Solution on Azure

Course Modality

Instructor-led (classroom)

Course Level

Intermediate

Course Time

3 days

Course Language

English

Course Overview

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.

Prerequisites

Before attending this course, students must have:

  • A fundamental knowledge of Microsoft Azure
  • Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib.
  • Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.

Intended 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.

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Course Curriculum

Introduction to Azure Machine Learning

In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.

Lessons

  • Getting Started with Azure Machine Learning
  • Azure Machine Learning Tools

 

Lab : Creating an Azure Machine Learning Workspace

Lab : Working with Azure Machine Learning Tools

After completing this module, you will be able to

  • Provision an Azure Machine Learning workspace
  • Use tools and code to work with Azure Machine Learning

No-Code Machine Learning with Designer

This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.

Lessons

  • Training Models with Designer
  • Publishing Models with Designer

 

Lab : Creating a Training Pipeline with the Azure ML Designer

Lab : Deploying a Service with the Azure ML Designer

After completing this module, you will be able to

  • Use designer to train a machine learning model
  • Deploy a Designer pipeline as a service

Running Experiments and Training Models

In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.

Lessons

  • Introduction to Experiments
  • Training and Registering Models

 

Lab : Running Experiments

Lab : Training and Registering Models

Recommended Exams

Exam DP-100

Designing and Implementing a Data Science Solution on Azure

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