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Sách Kubeflow for Machine Learning From Lab to Production (sách keo gáy, bìa mềm)
Categories:Computers - Computer Science
Year:2020
Edition:1
Language:english
Pages:264
If you're training a machine learning model but aren't sure how to put
it into production, this book will get you there. Kubeflow provides a
collection of cloud native tools for different stages of a model's
lifecycle, from data exploration, feature preparation, and model
training to model serving. This guide helps data scientists build
production-grade machine learning implementations with Kubeflow and
shows data engineers how to make models scalable and reliable.
Using
examples throughout the book, authors Holden Karau, Trevor Grant, Ilan
Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow
to train and serve your machine learning models on top of Kubernetes in
the cloud or in a development environment on-premises.
• Understand Kubeflow's design, core components, and the problems it solves
• Understand the differences between Kubeflow on different cluster types
• Train models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache Spark
• Keep your model up to date with Kubeflow Pipelines
• Understand how to capture model training metadata
• Explore how to extend Kubeflow with additional open source tools
• Use hyperparameter tuning for training
• Learn how to serve your model in production
