Data Science on the Google Cloud Platform. Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning (ebook)(audiobook)(audiobook) Książka w języku angielskim

Autor:
Valliappa Lakshmanan
Okładka książki/ebooka Data Science on the Google Cloud Platform. Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning

Okładka książki Data Science on the Google Cloud Platform. Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning

Okładka książki Data Science on the Google Cloud Platform. Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning

Okładka książki Data Science on the Google Cloud Platform. Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning

Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
410
2w1 w pakiecie:
     ePub
     Mobi

Ebook

199,00 zł
169,15 zł

Dodaj do koszyka lub Kup na prezent Kup 1-kliknięciem

Przenieś na półkę

Do przechowalni

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
0 Valliappa Lakshmanan

Zamknij

Wybierz metodę płatności