Book Image

Azure Data Scientist Associate Certification Guide

By : Andreas Botsikas, Michael Hlobil
Book Image

Azure Data Scientist Associate Certification Guide

By: Andreas Botsikas, Michael Hlobil

Overview of this book

The Azure Data Scientist Associate Certification Guide helps you acquire practical knowledge for machine learning experimentation on Azure. It covers everything you need to pass the DP-100 exam and become a certified Azure Data Scientist Associate. Starting with an introduction to data science, you'll learn the terminology that will be used throughout the book and then move on to the Azure Machine Learning (Azure ML) workspace. You'll discover the studio interface and manage various components, such as data stores and compute clusters. Next, the book focuses on no-code and low-code experimentation, and shows you how to use the Automated ML wizard to locate and deploy optimal models for your dataset. You'll also learn how to run end-to-end data science experiments using the designer provided in Azure ML Studio. You'll then explore the Azure ML Software Development Kit (SDK) for Python and advance to creating experiments and publishing models using code. The book also guides you in optimizing your model's hyperparameters using Hyperdrive before demonstrating how to use responsible AI tools to interpret and debug your models. Once you have a trained model, you'll learn to operationalize it for batch or real-time inferences and monitor it in production. By the end of this Azure certification study guide, you'll have gained the knowledge and the practical skills required to pass the DP-100 exam.
Table of Contents (17 chapters)
1
Section 1: Starting your cloud-based data science journey
6
Section 2: No code data science experimentation
9
Section 3: Advanced data science tooling and capabilities

Tracking data science assets in Azure ML Studio

Within the assets section, you can track all the components that are at the heart of machine learning operations. Every data science project has the following assets:

  • Datasets is where you can find registered datasets. This is a centralized registry where you can register your datasets and avoid colleagues having to work on local copies of the same data or, even worse, subsets of this data. You will work with datasets in Chapter 4, Configuring the Workspace.
  • Experiments is a centralized place to track groups of script executions or runs. When you are training a model, you are logging various aspects of that process, including metrics that you might need to compare performance. To group all attempts under the same context, you should submit all the runs under the same experiment name; then, the results will appear in this area. You will work with experiments in Chapter 5, Letting the Machines Do the Model Training.
  • Pipelines...