| Description |
1 online resource (1 volume) : illustrations |
| Summary |
Despite the enthusiasm for AI, a recent survey reveals that few organizations have adopted this technology in any meaningful way. One reason is the tedious process of accessing, labeling, and transforming data--known as data wrangling--that consumes 80% of a data scientist's time. There's a better way to model data. This practical ebook explores a new crop of self-service data preparation tools and services, including IBM Watson Studio, for automating data wrangling. Featuring interviews with data analytics experts, including Alex Castrounis (InnoArchiTech), Jay Limburn (IBM), and Katharine Jarmul (Data Wrangling with Python) among others, this ebook examines key barriers to capturing value from AI processes, including problems with data access and preparation. Author Kate Shoup shows data scientists and analysts how Watson Studio and other self-service tools enable domain experts, developers, and business analysts to practice data science. Get an overview of data science and learn how it differs from data analysis Examine a typical AI workflow, including challenges that surface at each step of the data wrangling process Learn solutions to AI workflow challenges, including the use of data catalogs to help you access and prep data Dive into Watson Studio and learn how the platform combines data catalogs with support for data science tools in one environment Evaluate a Watson Studio example and explore real-world use cases |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from cover (Safari, viewed November 5, 2019) |
| Subject |
Artificial intelligence -- Data processing.
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Artificial intelligence -- Industrial applications
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Electronic data processing -- Distributed processing.
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Artificial intelligence -- Data processing.
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Artificial intelligence -- Industrial applications.
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Electronic data processing -- Distributed processing.
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| Form |
Electronic book
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