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- 393pages
- 14 heures de lecture
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Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science. You'll learn how to: Automate and schedule data ingest, using an App Engine application Create and populate a dashboard in Google Data Studio Build a real-time analysis pipeline to carry out streaming analytics Conduct interactive data exploration with Google BigQuery Create a Bayesian model on a Cloud Dataproc cluster Build a logistic regression machine-learning model with Spark Compute time-aggregate features with a Cloud Dataflow pipeline Create a high-performing prediction model with TensorFlow Use your deployed model as a microservice you can access from both batch and real-time pipelines
Achat du livre
Data Science on the Google Cloud Platform, Valliappa Lakshmanan
- Langue
- Année de publication
- 2019
- Reliure
- (souple),
- État du livre
- Bon
- Prix
- 9,39 €
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- Titre
- Data Science on the Google Cloud Platform
- Sous-titre
- Implementing End-to-End Real-Time Data Pipelines - Updated for TensorFlow 2.0
- Langue
- Anglais
- Auteurs
- Valliappa Lakshmanan
- Éditeur
- O'Reilly Media
- Publié
- 2019
- Format
- souple
- Pages
- 393
- ISBN10
- 1491974567
- ISBN13
- 9781491974568
- Séries
- Mots clés
- Technologie, Littérature spécialisée, Intelligence Artificielle, Base de données, Analyse de données, Apprentissage automatique, Google
- Description
- Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science. You'll learn how to: Automate and schedule data ingest, using an App Engine application Create and populate a dashboard in Google Data Studio Build a real-time analysis pipeline to carry out streaming analytics Conduct interactive data exploration with Google BigQuery Create a Bayesian model on a Cloud Dataproc cluster Build a logistic regression machine-learning model with Spark Compute time-aggregate features with a Cloud Dataflow pipeline Create a high-performing prediction model with TensorFlow Use your deployed model as a microservice you can access from both batch and real-time pipelines