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portada Building Machine Learning Pipelines: Automating Model Life Cycles With Tensorflow
Type
Physical Book
Author
Publisher
Year
2020
Language
English
Pages
338
Format
Paperback
Dimensions
23.3x17.8x1.9 cm
Weight
0.58 kg.
ISBN13
9781492053194
Edition No.
1
Categories

Building Machine Learning Pipelines: Automating Model Life Cycles With Tensorflow

Hannes Hapke (Author) · Catherine Nelson (Author) · O'Reilly Media · Paperback

Building Machine Learning Pipelines: Automating Model Life Cycles With Tensorflow - Hapke, Hannes ; Nelson, Catherine

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Delivery: 18 Nov - 26 Nov Shipping: 14 to 15 business days.
NT$ 2,327
Used BookGood Condition Imported to Taiwan
Delivery: 12 Nov - 26 Nov Shipping: 10 to 15 business days.
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Synopsis "Building Machine Learning Pipelines: Automating Model Life Cycles With Tensorflow "

Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can 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 to successfully productize their data science projects, while managers will better understand the role they play 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

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