Scaling machine learning with Spark : distributed ML with MLlib, TensorFlow, and PyTorch
"Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you...
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| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo :
O'Reilly,
2023
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| Genre/form: | příručky |
| ISBN: | 978-1-098-10682-9 |
| Subjects: | |
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| Summary: | "Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better. Scaling machine learning with Spark examines several technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLflow, TensorFlow, and PyTorch. If you're a data scientist who works with machine learning, this book shows you when and why to use each technology. You will: Explore machine learning, including distributed computing concepts and terminology ; Manage the ML lifecycle with MLflow ; Ingest data and perform basic preprocessing with Spark ; Explore feature engineering, and use Spark to extract features ; Train a model with MLlib and build a pipeline to reproduce it ; Build a data system to combine the power of Spark with deep learning ; Get a step-by-step example of working with distributed TensorFlow ; Use PyTorch to scale machine learning and its internal architecture."--Nakladatelská anotace |
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| Item Description: | Obsahuje rejstřík |
| Physical Description: | xix, 270 stran : ilustrace ; 24 cm |
| Edition: | First edition |