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You're reading from  Machine Learning Infrastructure and Best Practices for Software Engineers

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Published inJan 2024
Reading LevelIntermediate
PublisherPackt
ISBN-139781837634064
Edition1st Edition
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Miroslaw Staron
Miroslaw Staron
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Miroslaw Staron

Miroslaw Staron is a professor of Applied IT at the University of Gothenburg in Sweden with a focus on empirical software engineering, measurement, and machine learning. He is currently editor-in-chief of Information and Software Technology and co-editor of the regular Practitioner's Digest column of IEEE Software. He has authored books on automotive software architectures, software measurement, and action research. He also leads several projects in AI for software engineering and leads an AI and digitalization theme at Software Center. He has written over 200 journal and conference articles.
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Data collection

Data collection is a procedure of transforming data from its raw format to a format that a machine learning algorithm can take as input. Depending on the data and the algorithm, this process can take different forms, as illustrated in Figure 2.3:

Figure 2.3 – Different forms of data collection – examples

Figure 2.3 – Different forms of data collection – examples

Data from images and measurements such as time series is usually collected to make classifications and predictions. These two classes of problems require the ground truth to be available, which we saw as Y_train in the previous code example. These target labels are either extracted automatically from the raw data or added manually through the process of labeling. The manual process is time-consuming, so the automated one is preferred.

The data that’s used in non-supervised learning and reinforcement learning models is often extracted as tabular data without labels. This data is used in the decision process or the...

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Machine Learning Infrastructure and Best Practices for Software Engineers
Published in: Jan 2024Publisher: PacktISBN-13: 9781837634064

Author (1)

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Miroslaw Staron

Miroslaw Staron is a professor of Applied IT at the University of Gothenburg in Sweden with a focus on empirical software engineering, measurement, and machine learning. He is currently editor-in-chief of Information and Software Technology and co-editor of the regular Practitioner's Digest column of IEEE Software. He has authored books on automotive software architectures, software measurement, and action research. He also leads several projects in AI for software engineering and leads an AI and digitalization theme at Software Center. He has written over 200 journal and conference articles.
Read more about Miroslaw Staron