Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning
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
- ISBN 13 : 1491974516
- ISBN 10 : 9781491974513
- Judul : Data Science on the Google Cloud Platform
- Sub Judul : Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning
- Pengarang : Valliappa Lakshmanan,
- Kategori : Computers
- Penerbit : "O'Reilly Media, Inc."
- Bahasa : en
- Tahun : 2017
- Halaman : 410
- Halaman : 410
- Google Book : https://play.google.com/store/books/details?id=MbpCDwAAQBAJ&source=gbs_api
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.