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

In this chapter, you got an overview of the various ways you can create an ML model in the AzureML workspace. You started with a simple regression model that was trained within the Jupyter notebook's kernel process. You learned how you can keep track of the metrics from the models you train. Then, you scaled the training process into the cpu-sm-cluster compute cluster you created in Chapter 7, The AzureML Python SDK. While scaling out to a remote compute cluster, you learned what the AzureML environments are and how you can troubleshoot remote executions by looking at the logs.

In the next chapter, you will build on this knowledge and use multiple computer nodes to perform a parallelized hyperparameter tuning process, which will locate the best parameters for your model. You will also learn how you can completely automate the model selection, training, and tuning using the AutoML capabilities of the AzureML SDK.