Scaling Machine Learning with Spark: Distributed ML with Mllib, Tensorflow, and Pytorch Polak Adi
Get up to speed on Apache Spark, the popular engine for large-scale data processing, including machine learning and analytics. Using Spark as your main data processing platform, you'll discover several open source technologies designed and built for enriching Spark's ML… Celý popis ↓
Porovnanie cien Scaling Machine Learning with Spark: Distributed ML with Mllib, Tensorflow, and Pytorch Polak Adi
Popis Scaling Machine Learning with Spark: Distributed ML with Mllib, Tensorflow, and Pytorch Polak Adi
Scaling Machine Learning with Spark: Distributed ML with Mllib, Tensorflow, and Pytorch Polak Adi
Get up to speed on Apache Spark, the popular engine for large-scale data processing, including machine learning and analytics. Using Spark as your main data processing platform, you'll discover several open source technologies designed and built for enriching Spark's ML capabilities.Scaling Machine Learning with Spark examines various technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLFlow, TensorFlow, PyTorch, and Petastorm. If you're looking to expand your skill set or advance your career in scalable machine learning with MLlib, distributed PyTorch, and distributed TensorFlow, this practical guide is for you.
If you're a data scientist working with machine learning, you'll learn how to:Build practical distributed machine learning workflows, This book shows you when to use each technology and why.
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