Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow

October 31, 2020
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 youthrough the steps of automating a machine learning pipeline usingthe TensorFlow ecosystem. You'll learn the techniques and toolsthat will cut deployment time from days to minutes, so that you canfocus on developing new models rather than maintaining legacysystems.Data scientists, machine learning engineers, and DevOpsengineers will discover how to go beyond model development tosuccessfully productize their data science projects, while managerswill better understand the role they play in helping to acceleratethese projects.Understand the steps to build a machine learning pipelineBuild your pipeline using components from TensorFlowExtendedOrchestrate your machine learning pipeline with Apache Beam,Apache Airflow, and Kubeflow PipelinesWork with data using TensorFlow Data Validation and TensorFlowTransformAnalyze a model in detail using TensorFlow Model AnalysisExamine fairness and bias in your model performanceDeploy models with TensorFlow Serving or TensorFlow Lite formobile devicesLearn privacy-preserving machine learning techniques