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Scaling machine learning with Spark
Title statement Scaling machine learning with Spark : distributed ML with MLlib, TensorFlow, and PyTorch / Adi Polak Personal name Polak, Adi (author) Edition statement First edition Publication Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo : O'Reilly, 2023 Phys.des. xix, 270 stran : ilustrace ; 24 cm ISBN 978-1-0981-0682-9 (brožováno) Note Obsahuje rejstřík Subj. Headings otevřený software open source software * frameworky software frameworks * big data big data * zpracování dat data processing * učící se systémy learning systems * strojové učení machine learning * Apache (software) Apache (software) Form, Genre příručky handbooks and manuals Conspect 004.4/.6 - Programování. Software UDC 004.42 , 004.6-022.257 , 004.62 , 004.85 , 004.42Apache , 004.4.057.8 , (035) Country Čína ; Spojené státy americké ; Velká Británie ; Japonsko Language angličtina Document kind Books Call number Barcode Location Sublocation Info 820:030/409 (KUP) 3139327365 Zbrojnice Ústřední knihovna UP - technika a průmysl In-Library Use Only
"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
Number of the records: 1