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
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
Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch
294
Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch
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Product Details
| ISBN-13: | 9781098106775 |
|---|---|
| Publisher: | O'Reilly Media, Incorporated |
| Publication date: | 03/07/2023 |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| Pages: | 294 |
| File size: | 7 MB |