Machine learning design patterns : solutions to common challenges in data preparation, model building, and MLOps / Valliappa Lakshmanan, Sara Robinson, and Michael Munn.
By: Lakshmanan, Valliappa [author.].
Contributor(s): Robinson, Sara [author] | Munn, Michael [author].
Publisher: Sebastopol, CA : O'Reilly Media, ©2020Copyright date: ©2020Edition: First edition.Description: 390 p: illustrations ; 23 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781098115784; 1098115783.Subject(s): Machine learning | Big data | Big data | Machine learningGenre/Form: Print books.| Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|
| On Shelf | Q325.5 .L35 2020 (Browse shelf) | Available | AU00000000018667 |
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Includes index.
The need for machine learning design patterns -- Data representation design patterns -- Problem representation design patterns -- Model training patterns -- Design patterns for resilient serving -- Reproducibility design patterns -- Responsible AI -- Connected patterns.
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.--