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Google focuses GCP on machine learning and data analytics

Google bet big in 2016 on machine learning and data analytics as differentiators for its cloud platform to make a stronger case to enterprise customers.

Google dispelled any lingering questions about its commitment to cloud in 2016, as its strategy emerged to become...

a major player in the enterprise market in the years ahead.

The company has spent tens of billions of dollars to build the underlying infrastructure, services and talent pool that feed Google Cloud Platform (GCP). As a result, it has de-emphasized price as the main differentiator and instead focused on enterprise demands, data analytics and a set of technologies to drive applications the company said will dominate the industry in the future.

Machine learning is still too new for many IT shops, but Google has banked on it as the future of cloud computing. New services added in 2016 included the Machine Intelligence suite of services and new releases for translation, text analysis, and image and speech recognition.

TensorFlow, the machine learning framework open-sourced in 2015, also got a boost from the Tensor Processing Unit. Tailored to TensorFlow, the custom application-specific integrated circuit has been used internally by Google for more than a year and promises considerably better performance per watt. In 2017, Google plans to add GPUs for its machine learning and Google Compute Engine customers, too.

Kubernetes, an open source version of Google's internal orchestration tool, solidified its position as one of the predominant technologies for containers this past year. Kubernetes clusters can be a challenge to set up and upgrade, so Google Container Engine (GKE) has gained traction, because it handles those duties and provides updates closely aligned with the Kubernetes release cycles.

"[GKE] scales, and it works," said Aaron Raddon, co-founder and CTO of Lytics, a Portland, Ore., startup and GCP customer that provides automation services for marketing. "It's hassle-free and hard to compete with."

Vendasta Technologies, which builds sales and marketing software for media companies, was a heavy user of Google App Engine, but it has moved to a microservices model and GKE. The company switched APIs from JSON to gRPC, which greatly improved documentation and delivery for customers, said Dale Hopkins, chief architect for the Saskatoon, Sask., company.

"We don't have everything in containers, but we're pretty darn close," he said.

Enterprise inroads set stage for 2017 and beyond

Diane Greene, former VMware co-founder and CEO, was hired in November 2015 for her enterprise bona fides -- a lack of which has been a constant knock against Google -- and began to put her stamp on the platform in 2016.

Google invested heavily in global expansion to address proximity and data residency issues, with two new regions added in 2016 to bring its total to six. The company plans to open a new region every month in 2017.

Google scored high-profile wins with Spotify and Evernote, and Citi, Goldman Sachs and other financial-sector heavyweights have endorsed the technology. Other big-name customers include Coca-Cola, Disney, Macy's and Sony -- but Google still lacks a flagship traditional enterprise to publicly tout as all-in on the platform.

To specifically address the enterprise market, Google and Accenture will develop industry-specific services for retail, healthcare, energy, finance and other sectors.

They're trying to kind of redouble or triple those efforts to engage more partners and more customers.
Michael Liebowglobal managing director, Accenture Cloud

"You're starting to see more of a commitment to the market and engaging the market," said Michael Liebow, global managing director of Accenture Cloud. "They're trying to kind of redouble or triple those efforts to engage more partners and more customers."

New security features important to enterprises were added for identity and access management and customer-supplied encryption keys, while international compliance standards were met to protect customer data.

Multiple acquisitions in 2016 bolstered GCP -- the most notable was Apigee Corp., which offers a range of API management services seen as more enterprise-friendly than what comes native on Amazon Web Services (AWS).

Google grouped several disparate business units under the new umbrella of Google Cloud: GCP, the renamed G Suite of apps, machine learning tools, APIs and Google devices that connect to the cloud. A new limited-access consulting service, Customer Reliability Engineering, provides a shared responsibility model for application reliability, and it is intended to help Google and enterprise customers learn a bit of operational know-how from each other.

Of course, Google still has plenty of work to do if it wants to catch the competition and address enterprise demands. Industry observers generally lump it in with Amazon Web Services and Microsoft Azure as one of the big three hyperscale providers, though it's seen as a distant third.

Unlike Amazon, which has advised against multicloud strategies, Google needs that model to steal away some of AWS' massive lead in the market. To that end, it has pushed Kubernetes and various services such as Stackdriver, which monitors, logs and performs diagnostics for applications on GCP and AWS.

Google also has encountered growing pains that AWS experienced in its early years and Microsoft Azure appears to have now left behind. Most notably, GCP experienced multiple outages and service disruptions due to networking failures in 2016.

Big data still a big deal at Google

Data analytics remains the lynchpin for GCP, with services such as Dataflow, Dataproc and Pub/Sub. The BigQuery service -- an oft-cited differentiator for Google customers -- was upgraded this year with support for StandardSQL and integration with tools such as Microsoft Excel, improved monitoring and fine-grained security policies, and simplified date-specific data partitions.

Other additions brought multiregional storage and streamlined storage classes. The company added lifecycle management to automate data transitions over time, in addition to Coldline, which provides cheap cold storage with fast retrieval times.

In addition to its use of GKE, Lytics is pleased with the continued improvements of big data services and machine learning tools GCP has incorporated into its workloads, including Bigtable, Pub/Sub and Vision API. Google has steadily improved with a simplified, feature-rich core set of services, rather than expand too many different services, Raddon said.

"More and more of our core platform is built on top of Google Cloud, and it just keeps getting better and better," he said.

Trevor Jones is a news writer with SearchCloudComputing and SearchAWS. Contact him at tjones@techtarget.com.

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