Scaling Machine Learning with Spark Distributed ML with MLlib, TensorFlow, and PyTorch

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Free Download Scaling Machine Learning with Spark
by Adi Polak;

English | 2023 | ISBN: 1098106822 | 294 pages | True PDF | 7.61 MB

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 terminologyManage the ML lifecycle with MLflowIngest data and perform basic preprocessing with SparkExplore feature engineering, and use Spark to extract featuresTrain a model with MLlib and build a pipeline to reproduce itBuild a data system to combine the power of Spark with deep learningGet a step-by-step example of working with distributed TensorFlowUse PyTorch to scale machine learning and its internal architecture



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Machine Learning with Spark ML
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 2h 7m | 454 MB
Instructor: Ivan Mushketyk​

Learn how to use Spark ML to build scalable machine learning solutions. Get hands-on with regression, classification, feature engineering, model evaluation, hyperparameter tuning and more, plus learn deep learning integration with Apache Spark.

What you'll learn

  • Use Spark ML to build scalable machine learning models
  • Apply regression and classification algorithms
  • Apply feature engineering techniques at scale
  • Evaluate model performance with the right metrics
  • Optimize models through hyperparameter tuning
  • Integrate deep learning tools with Spark
  • Handle big data Machine Learning workflows efficiently
  • Build production-ready Machine Learning pipelines

Machine learning doesn't stop at theory.it needs to scale. In this course, you'll learn how to take ML models to production-level scale using Spark ML.

We'll dive into hands-on techniques for regression and classification, explore smart feature engineering, and show you how to evaluate and tune models for real performance. You'll also get a glimpse of how deep learning can plug into your Spark workflows.

If you're ready to stop experimenting and start building, this is where it happens.

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