Hannes Hapke,Catherine Nelson
Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow
Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow
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- More about Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow
Companies are investing billions in machine learning projects, but if the models cannot be deployed effectively, the money is wasted. This practical guide will help you automate a machine learning pipeline using the TensorFlow ecosystem, reducing deployment time from days to minutes. It will teach you techniques and tools to productize your data science projects, and how managers can help accelerate them.
\n Format: Paperback / softback
\n Length: 366 pages
\n Publication date: 28 July 2020
\n Publisher: O'Reilly Media, Inc, USA
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Companies are investing billions in machine learning projects, yet the money is being wasted if the models cannot be effectively deployed. In this comprehensive guide, Hannes Hapke and Catherine Nelson take you through the process of automating a machine learning pipeline using the TensorFlow ecosystem. You will learn the techniques and tools that will reduce deployment time from days to minutes, allowing you to focus on developing new models rather than maintaining legacy systems.
Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development and successfully productize their data science projects. Managers will gain a better understanding of their role in helping to accelerate these projects.
Understand the steps to build a machine learning pipeline:
Build your pipeline using components from TensorFlow Extended.
Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines.
Work with data using TensorFlow Data Validation and TensorFlow Transform.
Analyze a model in detail using TensorFlow Model Analysis.
Examine fairness and bias in your model performance.
Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices.
Learn privacy-preserving machine learning techniques.
By following these steps, you can streamline your machine learning pipeline and ensure that your models are deployed efficiently, allowing you to reap the benefits of this powerful technology.
\n Weight: 642g\n
Dimension: 179 x 232 x 23 (mm)\n
ISBN-13: 9781492053194\n \n
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