Description
Book Synopsis: 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
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Details
Looking to take your machine learning projects to the next level? Look no further than the "Scaling Machine Learning with Spark" book! Written by acclaimed author Adi Polak, this practical guide introduces data and ML practitioners to cutting-edge solutions that go beyond traditional methods.
With "Scaling Machine Learning with Spark," you'll gain a more comprehensive understanding of machine learning, distributed computing concepts, and terminology. The book will show you how to effectively manage the ML lifecycle using MLflow, a powerful tool for tracking and managing experiments.
But that's not all! "Scaling Machine Learning with Spark" also delves into feature engineering and how to leverage Spark to extract valuable features. You'll discover how to train models using Spark's MLlib and build pipelines to reproduce your results with ease.
What sets this book apart is its focus on integrating Spark with other technologies like TensorFlow and PyTorch for distributed machine learning. You'll get a step-by-step example of working with distributed TensorFlow, as well as insights into scaling machine learning with PyTorch and its internal architecture.
Ready to revolutionize your machine learning workflows? Don't miss out on "Scaling Machine Learning with Spark." Grab your copy today and unlock the secrets to building scalable machine learning solutions. Click here to get your copy now!
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