Using the ePub ebook “Data Science on the Google Cloud Platform: 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 easy 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 ebook, you will work through a sample business decision by employing a variety of data science approaches.
Follow along by implementing these machine learning and statistical solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science.
You will learn how to:
- Create a Bayesian model on a Cloud Dataproc cluster
- Create and populate a dashboard in Google Data Studio
- Conduct interactive data exploration with Google BigQuery
- Create a high-performing prediction model with TensorFlow
- Make a logistic regression machine-learning model with Spark
- Compute time-aggregate features with a Cloud Dataflow pipeline
- Build a real-time analysis pipeline to carry out streaming analytics
- Automate and schedule data ingest, using an App Engine application
- Use your deployed model as a microservice you can access from both batch and real-time pipelines
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