Building Machine Learning Pipelines

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. The book also explores new approaches for integrating data privacy into machine learning pipelines. Understand the machine learning management lifecycle Implement data pipelines; Build your pipeline using components from TensorFlow extended; Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow 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.