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

Summary

Azure Machine Learning Studio provides a web environment where you can manage all the artifacts in your Azure Machine Learning workspace. You can view and manage your Jupyter notebooks, datasets, experiments, pipelines, models, and endpoints. You can also manage the compute resources and datastores that will be used in your experiments. Studio also offers interactive tools you can use to perform no-code data science experiments, something you will deep dive into in the next chapters of this book. The AutoML wizard is the first no-code experience that's baked into Azure ML Studio and allows you to run automated machine learning experiments. Azure Machine Learning designer is the next no-code experience and helps you graphically design pipelines and create workflows without writing code. This experience also enables low-code scenarios, where you can drop code snippets if needed. Finally, data labeling projects allow you to create, manage, and monitor tedious projects to...