Building Machine Learning Pipelines (häftad)
Fler böcker inom
Format
E-bok
Filformat
PDF med LCP-kryptering (0.0 MB)
Om LCP-kryptering
PDF-böcker lämpar sig inte för läsning på små skärmar, t ex mobiler.
Nedladdning
Kan laddas ned under 24 månader, dock max 6 gånger.
Språk
Engelska
Antal sidor
366
Utgivningsdatum
2020-07-13
Förlag
O'Reilly Media
ISBN
9781492053163

Building Machine Learning Pipelines E-bok

av Hannes Hapke, Catherine Nelson
E-bok (PDF, LCP),  Engelska, 2020-07-13
629
Ladda ned
Spara som favorit
Läs i Bokus Reader för iOS och Android
Finns även som
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

Kundrecensioner

Har du läst boken? Sätt ditt betyg »

Fler böcker av författarna

  • Software Engineering for Data Scientists

    Software Engineering for Data Scientists

    Catherine Nelson

    Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success-and is absolutely essential for those working with production code. This practical book bridges the gap between data...

    668
    Köp
    Skickas inom vardagar.
  • Machine Learning Production Systems

    Machine Learning Production Systems

    Robert Crowe, Hannes Hapke, Emily Caveness, Di Zhu

    Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research settingespecially for recent ML graduates and those moving from research to a commercial environment. Whethe...

    836
    Ladda ned
    Laddas ned direkt

Du kanske gillar