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ML Scalability Handling Big Data with Efficiency: Scale ML models for large datasets and high-performance tasks by Isandro Myles

ML Scalability Handling Big Data with Efficiency: Scale ML models for large datasets and high-performance tasks

by Isandro Myles
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Book cover type: Paperback
  • ISBN13: 9798264687761
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Publication Date:
  • Pages: 174
  • Original Price: USD 16.99
  • Language: English
  • Edition: N/A
  • Item Weight: 241 grams
  • BISAC Subject(s): Programming / Algorithms

Unlock the power of scalable machine learning to handle big data.

In ML Scalability, you'll learn how to scale machine learning models to efficiently handle large datasets and high-performance tasks. This practical guide will show you how to optimize your machine learning workflows, build scalable solutions, and apply advanced techniques to tackle complex problems that require massive amounts of data.

Inside, you'll discover how to:

  • Understand ML scalability: Learn why scalability is crucial in modern machine learning and how it impacts performance, data processing, and model deployment.

  • Scale machine learning models for big data using distributed frameworks like Apache Spark, Dask, and Hadoop for parallel processing.

  • Build efficient pipelines that process and clean massive datasets using pandas, PySpark, and TensorFlow Data API.

  • Implement distributed training strategies with multi-GPU/TPU setups and data parallelism for faster model training.

  • Optimize data storage and access patterns for large datasets with HDF5, Parquet, and Apache Arrow to streamline workflows.

  • Use cloud platforms like AWS, Google Cloud, and Azure ML to scale models and integrate with other big data tools.

  • Learn model performance optimization techniques such as batch processing, mini-batch gradient descent, and distributed learning.

  • Apply scalable algorithms for tasks such as regression, classification, and clustering that work efficiently at scale.

  • Implement model serving and deployment strategies using TensorFlow Serving, KubeFlow, and MLflow for scalable production environments.

  • Use hyperparameter tuning and automated machine learning (AutoML) techniques to further optimize model performance in large-scale settings.

Packed with step-by-step tutorials, real-world examples, and best practices, this book empowers you to tackle big data challenges and scale your machine learning models to handle massive datasets efficiently.

Who This Book Is For

  • Data scientists and machine learning engineers seeking to optimize and scale their models for large datasets

  • Cloud architects and engineers looking to leverage cloud infrastructure for scalable ML solutions

  • Researchers and students focused on scaling machine learning for high-performance tasks

  • Developers working with big data and looking to optimize machine learning workflows

  • Business professionals looking to apply scalable ML solutions to handle large-scale data problems

Master the techniques to scale your machine learning models and process big data efficiently for high-performance results.

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