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

SS Course: GK100327

Course Overview

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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.

One Microsoft exam voucher included with class.

                                                                  

Scheduled Classes

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02/20/23 - GVT - Virtual Classroom - Virtual Instructor-Led
04/03/23 - GVT - Virtual Classroom - Virtual Instructor-Led
06/05/23 - GVT - Virtual Classroom - Virtual Instructor-Led

Outline

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Module 1 : Introduction to the Azure Machine Learning SDK

  • Provision an Azure Machine Learning workspace.
  • Use tools and interfaces to work with Azure Machine Learning.
  • Run code-based experiments in an Azure Machine Learning workspace.

Module 2 : Use Automated Machine Learning in Azure Machine Learning

  • Learn how to use the automated machine learning user interface in Azure Machine Learning

Module 3 : Create a classification model with Azure Machine Learning designer

  • Train and publish a classification model with Azure Machine Learning designer

Module 4 : Train a machine learning model with Azure Machine Learning

  • Use a ScriptRunConfig to run a model training script as an Azure Machine Learning experiment.
  • Create reusable, parameterized training scripts.
  • Register trained models.

Module 5 : Work with Data in Azure Machine Learning

  • Create and use datastores in an Azure Machine Learning workspace.
  • Create and use datasets in an Azure Machine Learning workspace.

Module 6 : Work with Compute in Azure Machine Learning

  • Work with environments
  • Work with compute targets

Module 7 : Orchestrate machine learning with pipelines

  • Create Pipeline steps
  • Pass data between steps
  • Publish and run a pipeline
  • Schedule a pipeline

Module 8 : Deploy real-time machine learning services with Azure Machine Learning

  • Deploy a model as a real-time inferencing service.
  • Consume a real-time inferencing service.
  • Troubleshoot service deployment

Module 9 : Deploy batch inference pipelines with Azure Machine Learning

  • Learn how to create, publish, and use batch inference pipelines with Azure Machine Learning.

Module 10 : Tune hyperparameters with Azure Machine Learning

  • Learn how to use Azure Machine Learning hyperparameter tuning experiments to optimize model performance.

Module 11 : Automate machine learning model selection with Azure Machine Learning

  • Use Azure Machine Learning's automated machine learning capabilities to determine the best performing algorithm for your data.
  • Use automated machine learning to preprocess data for training.
  • Run an automated machine learning experiment.

Module 12 : Explore differential privacy

  • Articulate the problem of data privacy
  • Describe how differential privacy works
  • Configure parameters for differential privacy
  • Perform differentially private data analysis

Module 13 : Explain machine learning models with Azure Machine Learning

  • Learn how to explain models by calculating and interpreting feature importance.

Module 14 : Detect and mitigate unfairness in models with Azure Machine Learning

  • How to evaluate machine learning models for fairness.
  • How to mitigate predictive disparity in a machine learning model.

Module 15 : Monitor data drift with Azure Machine Learning

  • Learn how to monitor data drift in Azure Machine Learning.

Module 16 : Monitor models with Azure Machine Learning

  • Learn how to use Azure Application Insights to monitor a deployed Azure Machine Learning model.

    Prerequisites

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    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.

      Who Should Attend

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      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.