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Data Science on the Google Cloud Platform - Implementing end-to-end real-time data pipelines: from ingest to machine learning (Paperback)
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Data Science on the Google Cloud Platform - Implementing end-to-end real-time data pipelines: from ingest to machine learning (Paperback)
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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
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