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040			_c0
082			_a004.33 LAK
092			_20
100			_aLakshmanan, Valliappa
245			_aData science on the Google cloud platform : _bimplementing end-to-end real-time data pipelines : from ingest to machine learning _cValliappa Lakshmanan
260			_aSebastopol, CA _bO'Reilly _c2016
300			_axxiv, 492 pages : _billustrations (some color) _c23 cm
520			_aLearn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. You'll learn how to: Employ best practices in building highly scalable data and ML pipelines on Google Cloud Automate and schedule data ingest using Cloud Run Create and populate a dashboard in Data Studio Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery Conduct interactive data exploration with BigQuery Create a Bayesian model with Spark on Cloud Dataproc Forecast time series and do anomaly detection with BigQuery ML Aggregate within time windows with Dataflow Train explainable machine learning models with Vertex AI Operationalize ML with Vertex AI Pipelines
650			_aCloud computing
650			_aComputing platforms
843			_aPhotocopy
887			_2CamTech Library
942			_2ddc _cBK _n0
999			_c1010 _d1010