A key benefit of doing data science on the cloud is the amount of time that it saves you. You shouldn’t have to wait days or months — instead, because many jobs are parallel, you can get your results in minutes-to-hours by having them execute on thousands of machines. Running data jobs on thousands of machines for minutes at a time requires fully managed services. Given the choice between a product that requires you to first configure a container, server or cluster and another product that frees you from those considerations, the serverless option is always more ideal. You’ll have more time to solve the problems that actually matter to your business. In this video, Lak Lakshmanan, Alex Osterloh, and Rez Rokni walk through an example of carrying out a data science task from a Datalab notebook that marshals the auto-awesome power of Google Cloud Platform (GCP) — which includes Google Cloud Pub/Sub, Google Cloud Dataflow and Google BigQuery — to glean insights from your data.
Missed the conference? Watch all the talks here: https://goo.gl/c1Vs3h
Watch more talks about Big Data & Machine Learning here: https://goo.gl/OcqI9k
Publisher: Google Cloud
You can watch this video also at the source.