Google edges into cloud analytics, big data, machine learning alongside Amazon, IBM, Microsoft

The company unveiled Cloud Machine Learning for analysis, exploration, processing and storage of large data sets, services that could bring deep learning techniques to healthcare and fuel visions like precision medicine and population health.
By Jack McCarthy
04:43 PM

Google jumped into the emerging space for analytics and big data when it revealed the new Cloud Machine Learning suite of services.

"There's a new architecture emerging," Eric Schmidt, executive chairman of Google parent Alphabet, said at Google’s GCP Next last week. "In a year, you will use machine learning to do something better than humans have been doing. You'll do something new. You'll discover something new."

Schmidt is not alone in that thinking. Google rivals Amazon, IBM, and Microsoft, in fact, have made similar cloud computing moves of late.

The big four cloud computing giants all are working on analytics, big data, and machine learning, while IBM’s Watson supercomputer garners the most attention in healthcare and is currently being used in pilots at MD Anderson and Memorial Sloan Kettering.

Schmidt predicted that such cloud services will enable data management on an affordable scale not possible before.

Google’s Cloud Machine Learning is integrated with other offerings including BigQuery for processing large data sets, Cloud Dataflow for creating pipelines, Cloud DataLab for so-called data exploration, Cloud Storage, and DataProc, a managed services comprising Hadoop, MapReduce, Spark, Pig. 

“Cloud Machine Learning will take care of everything from data ingestion through prediction,” Google’s director of product management Fausto Ibarra wrote on Google’s site. “Now any application can take advantage of the same deep learning techniques that power many of Google’s services.”

The growing collection of technologies for analytics, big data and machine learning from multiple vendors are thought to have great potential in healthcare, with the capability to manage large data sets in myriad medical-related applications from clinical point-of-care settings to genomics and grand visions such as precision medicine and population health management. 

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