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Building Machine Learning Pipelines (Paperback)
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Building Machine Learning Pipelines (Paperback)
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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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