Building Machine Learning Pipelines

Hannes Hapke, Catherine Nelson

ca. 51,86
Amazon iTunes Hugendubel Bü kobo Osiander Google Books Barnes&Noble Legimi Kulturkaufhaus
* Affiliatelinks/Werbelinks
Hinweis: Affiliatelinks/Werbelinks
Links auf sind sogenannte Affiliate-Links. Wenn du auf so einen Affiliate-Link klickst und über diesen Link einkaufst, bekommt von dem betreffenden Online-Shop oder Anbieter eine Provision. Für dich verändert sich der Preis nicht.

O'Reilly Media img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Informatik, EDV


Companies are spending billions on machine learning projects, but its money wasted if the models cant 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. Youll 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 pipelineBuild your pipeline using components from TensorFlow ExtendedOrchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow PipelinesWork with data using TensorFlow Data Validation and TensorFlow TransformAnalyze a model in detail using TensorFlow Model AnalysisExamine fairness and bias in your model performanceDeploy models with TensorFlow Serving or TensorFlow Lite for mobile devicesLearn privacy-preserving machine learning techniques