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USED-Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time Data Pipelines: From Ingest to Machine Learning by Lakshmanan, Valliappa (Paperback)
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| Number Of Pages | 404 |
| Year | 2018 |
| Publisher | O'Reilly Media |
| Isbn/Issn | 9781491974568 |
| Author | Lakshmanan, Valliappa |
| Cover Type | Soft Cover |
| Language | English |
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About our used conditions ratings:·Like New: An apparently unread copy in excellent condition. The dust cover is intact, and the pages are clean and not marred by notes or folds of any kind.·Very Good: A copy that has been read, but remains in excellent condition. May have writing on the inside cover but pages are unmarred.·Good: A copy that has been read, but remains in clean condition. All pages and covers are intact. The spine may show signs of wear. Pages can include limited notes and highlighting, and the copy can include "From the library of" labels or previous owner inscriptions. 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 1.5 pounds
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