Training Data for Machine Learning

The success of a data project depends on the training data, and there are few comprehensive resources to help data science professionals and machine learning engineers ace the process. This hands-on guide explains how to work with and scale training data, including designing, deploying, and shipping training data for production-grade deep learning applications, integrating with a growing ecosystem of tools, recognizing and correcting new training data-based failure modes, improving existing system performance, and avoiding development risks. It also helps to avoid data loss by structuring metadata around created datasets and clearly explaining training data concepts to subject matter experts and other shareholders.

Format: Unspecified
Length: 329 pages
Publication date: 30 November 2023
Publisher: O'Reilly Media

The success of your data project hinges on the quality of your training data, just as much as the algorithms you employ. Unfortunately, many failures in deep learning systems can be attributed to inadequate or incorrect training data. While training data is the cornerstone of successful machine learning, there are limited resources available to guide you through the process effectively. This comprehensive hands-on guide aims to equip data science professionals and machine learning engineers with the knowledge and skills necessary to handle and scale training data for production-grade deep learning applications.

In this guide, you will learn how to design, deploy, and ship training data for deep learning models, seamlessly integrate with a growing ecosystem of tools, identify and rectify new training data-based failure modes, enhance the performance of existing systems, and confidently employ automation and acceleration approaches to create training data more efficiently. You will also gain insights into structuring metadata around created datasets to prevent data loss and effectively communicate training data concepts to subject matter experts and other stakeholders.

By following the steps outlined in this guide, you will be able to successfully maintain, operate, and improve your deep learning system, ensuring its long-term success and reliability. Whether you are a novice or an experienced data scientist, this guide will provide you with the tools and techniques you need to excel in your data projects.

ISBN-13: 9781492094524