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

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

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning.

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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.

Who Should Attend?

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.

Course Outline

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

Labs: Creating an Azure Machine Learning Workspace
Labs: Working with Azure Machine Learning Tools

  • Training Models with Designer
  • Publishing Models with Designer

Labs: Creating a Training Pipeline with the Azure ML Designer
Labs: Deploying a Service with the Azure ML Designer

  • Introduction to Experiments
  • Training and Registering Models

Labs: Running Experiments
Labs: Training and Registering Models

  • Working with Datastores
  • Working with Datasets

Labs: Working with Datastores
Labs: Working with Datasets

  • Working with Environments
  • Working with Compute Targets

Labs: Working with Environments
Labs: Working with Compute Targets

  • Introduction to Pipelines
  • Publishing and Running Pipelines

Labs: Creating a Pipeline
Labs: Publishing a Pipeline

  • Real-time Inferencing
  • Batch Inferencing

Labs: Creating a Real-time Inferencing Service
Labs: Creating a Batch Inferencing Service

  • Hyperparameter Tuning
  • Automated Machine Learning

Labs: Tuning Hyperparameters
Labs: Using Automated Machine Learning

  • Introduction to Model Interpretation
  • Using Model Explainers

Labs: Reviewing Automated Machine Learning Explanations
Labs: Interpreting Models

  • Monitoring Models with Application Insights
  • Monitoring Data Drift

Labs: Monitoring a Model with Application Insights
Labs: Monitoring Data Drift

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