Posted by Alex Barrett, Editor, Google Cloud Platform Blog


Evernote, the latest company to announce their move to Google Cloud Platform, said this week that part of the appeal of GCP is gaining access to “the same deep-learning technologies that power services like translation, photo management and voice search.” Evernote didn’t elaborate on exactly how machine learning might manifest in its productivity software, though, so we thought we’d share some other examples that we’ve come across.

First and foremost, who can forget Makoto Koike, the Japanese farmer who used the Google-developed machine learning library TensorFlow to learn to sort cucumbers according to complex traditional criteria?

Then there are the bright folks over at Google DeepMind and their paper on WaveNet, which generates speech that mimics the human voice with much more natural-sounding results than current text-to-speech systems. Or Google’s recent solutions document in which university art students “experiment with DeepDream algorithms to render digital artwork using machine intelligence.”

Meanwhile, Google Developer Advocate Sara Robinson has unearthed some very practical use cases for machine learning. Check out this post, in which she takes us on on a whirlwind tour of the Cloud Vision API to detect landmarks, and this post on how to use it to filter inappropriate content. She then embarks on a series of posts on using Google Cloud Natural Language with BigQuery. Here’s a post on analyzing twitter posts about the Rio Olympics, and another that compares tweets about Hillary Clinton and Donald Trump.

(Speaking of the Natural Language API, if you need a bit of a primer on how to integrate it into existing projects, check out this post from digital consultancy White October on how to connect Cloud Natural Language API with Python on Google App Engine. Thanks, guys, for as you put it, filling what was “a definite lack of a ‘hello world’ sample showing the basics of how to connect to and call the API.”)

But really, the use cases for machine learning are just early examples, and it’s anyone’s guess what tomorrow’s killer machine learning app will be (Diane Greene discusses some pretty compelling examples of using machine learning starting at the 10:00 minute mark).

Perhaps you’ll be the one to come up with the next great use case for machine learning? Increase your chances by signing up for the new Udacity class on deep learning. Over 61,000 students have already signed up for the free three-month class!

Posted by Alex Barrett, Editor, Google Cloud Platform Blog

Google has a long and storied history running Linux, but Google Cloud Platform’s goal is to support a broad range of languages and tools. This week saw us significantly expand our support for the Microsoft ecosystem, with new support for ASP.NET, SQL Server, Powershell and the like.

If you have apps developed in .NET, Microsoft’s application development framework, you’ll be happy to learn that you can run them efficiently on GCP, with support for several flavors of Windows Server, an ASP.NET image in Cloud Launcher, pre-loaded SQL Server images on Google Compute Engine, and a variety of Google APIs available for the .NET platform. And thanks to a new integration with Microsoft Visual Studio, the popular integrated development environment, developers in the Microsoft ecosystem can easily access that functionality from the comfort of their IDE.

But it’s not just about Google broadening its horizons. Microsoft, too, is taking its offerings outside of its traditional confines. This week, Microsoft open-sourced Powershell, the command-line shell and scripting language for .NET, so that developers can use it to automate and administer Linux apps and environments, not just Windows ones.

And Kubernetes, Google’s open-source container management system, is also finding its way over to Microsoft’s Azure public cloud, thanks to its ability to provide a lingua franca for hosting and managing container-based environments. Check out this blog post about provisioning Azure Kubernetes infrastructure to see just how far things have come.

Posted by Alex Barrett, Editor, Google Cloud Platform Blog

Ah, summer! The time for relaxing, taking the kids to a matinee, and . . . using machine learning to recognize everyday objects using the Cloud Vision API!

That’s what the fine folks at Disney and Google Zoo are doing to promote their new movie Pete’s Dragon: Accessing the Cloud Vision RESTful API, Disney has created a mobile website that allows your mobile device to recognize objects in your field of vision and display Elliot the Dragon in and around those objects in Augmented Reality (AR). Try it out from your mobile device at Dragonspotting.com.

But in Google Cloud Platform circles, that’s been the extent of the relaxing. In the past week, the GCP team has been exceptionally busy, releasing new versions of Google Cloud Dataflow and Google Cloud Datalab, adding support for Python 3 in Google App Engine flexible environment, acquiring Orbitera, partnering with Facebook on a new DC 48V power standard and dropping prices on Preemptible VMs!

Other community members chimed in on how to perform rolling updates on managed GCP databases, analyzing residential construction trends using Google BigQuery, exploring the performance model of Cloud DataFlow and analyzing GitHub pull requests using BigQuery.

Maybe all this hard work is paying off. A recent survey of 200 IT professionals found that 84% of them are using public cloud services, and that GCP beats out the other major providers as their preferred platform.

A survey by SADA Systems, a Google for Work Premier Partner, of 200+ IT managers about their use of public cloud services

OK, so maybe we’ll take a vacation next week . . .

Posted by Alex Barrett, Editor, Google Cloud Platform Blog

In case you hadn’t heard, we here at Google Cloud Platform released the Cloud Natural Language API this week, and an open beta of the Speech API.

Both the Natural Language and Speech APIs are just the latest examples in the Cloud Machine Learning technologies that we’ve made available to the public, following on the heels of the Vision API and Translate API. But what exactly do these latest APIs allow you to do?

Natural Language is all about parsing written text — you know, the kind that you’re looking at right now. By way of introduction, check out Google Developer Advocate Sara Robinson’s post on how she used the Natural Language API to analyze stories in The New York Times, while introducing us to the NL concepts of “sentiment” and “entities.”

Google Developer Advocate Guillaume Laforge dives deeper into sentiments by color coding tweets as strong positive, strongly negative — or somewhere in between — according to the polarity and magnitude unearthed by Natural Language. Turns out that @googlecloud tweets are all over the map, sentiment-wise, judging by this many-colored chart.

Positive tweets are green, negative tweets are red and neutral tweets are yellow

Others may choose to sample much less colorful text streams, such as Theresa May’s inaugural speech as British Prime Minister. In a blog, Javier Ramirez, a Google expert at Teowaki, uses the Speech API to convert the audio to text, then feeds it to Natural Language to analyze its entities and sentiments. “I never suspected Brexit could be this fun,” he writes.

But how reliable are these latest machine learning offerings? Make no mistake, it’s early days, and natural language processing is an imperfect science. Over on Hacker News, some people reported mixed results with Natural Language. Check out the conversation with Google Natural Language Product Manager Dave Orr, who explains why a sentence that is so easy for a human “wetware” brain to understand can still trip up a computer. “It's the curse of [natural language processing], really,” he says. “All the easy things are hard. (And the hard things are nigh impossible.)”

We hope you’ll be the judge. Scroll down to the bottom of the Cloud Natural Language API page, and enter a snippet of text and try the API.

Posted by Alex Barrett, Editor, Google Cloud Platform Blog

It’s been a couple of weeks since GitHub announced that it was making 3+TB of its open source library available on BigQuery, and the Google Cloud Platform community has been busy ever since.

Google Developer Advocate Felipe Hoffa showed the world how it was done in “GitHub on BigQuery: Analyze all the open source code,” and fellow DA Fransesc Campoy followed suit with a post analyzing GitHub Go packages. Along the way, he discovers that he can create even more nuanced queries by using BigQuery User Defined Functions.

Then, one of Google’s newest DAs Guillaume Laforge informs us that there are 743,070 Groovy files on GitHub with 16,464,376 lines of code, while CloudFlare’s Filippo Valsorda (the “Heartbleed guy”) analyzes how the Go ecosystem “does vendoring.”

Meanwhile, over on Medium, Google program manager for big data and machine learning Lak Lakshmanan uses BigQuery to discover which popular Java projects need the most help by searching for tagged comments such as FIXME and TODO. The post also shows how to use Google Cloud Dataflow to build a pipeline starting from BigQuery to Java in order to process the data in steps.

Or check out Robert Kozikowski’s blog for a treasure trove of GitHub data analysis: posts on visualizing relationships between python packages; top pandas, numpy and scipy functions, emacs packages and angular directives.

And if that’s still not enough BigQuery on GitHub for you, here’s a Changelog podcast on the topic for your drive home!

Posted by Alex Barrett, Editor, Google Cloud Platform Blog


Kubernetes has hit an important milestone — version 1.3 — and Google Container Engine, our managed version thereof, is moving along with it.

What does this latest version mean for Kubernetes users and GKE shops? Google Developer Advocate Carter Morgan takes a stab at laying it all out in this deck.

Meanwhile, the Kubernetes community is busy building out a collection of resources that show users how to use Kubernetes effectively. Just getting started? Arun Gupta of Couchbase has a tutorial to help you get started. You might also want to take a step back and read the paper on Kubernetes design patterns that Google’s Brendan Burns presented at Usenix last month.

For shops that are already all-in with Kubernetes, Google’s Kelsey Hightower presents on using Kubernetes to manage Redis, Java developer Eduard Kaiser digs deep into the Kubernetes Ingress Controller and Sandeep Dinesh tells us about how to get the new Docker Swarm up and running on GKE.

With this kind of momentum, it’s no surprise that the number of companies running on top of Kubernetes is starting to pile up. Check out the conversation on HackerNews about the good stuff that Kubernetes does for IT operations. Or The New Stack’s write-up about WePay, a PCI-certified credit card processing provider that has adopted containers and Kubernetes as it moves to microservices. And online gaming provider Rayark, whose smash-hit VOEZ runs almost entirely out of GKE (its Redis database runs in VMs on Google Compute Engine).

But we’re not done yet. Close your eyes and imagine a world where Kubernetes is running on Microsoft Azure. Now, open them and check out Kelsey Hightower’s demo of Kubernetes 1.4 running on Microsoft Azure. And be sure to sign up for our upcoming GKE usability study! Why stand by idly and watch, when you can shape the future directly?

Posted by Alex Barrett, Editor, Google Cloud Platform Blog

If you’re into big data or big bandwidth, or both, Google Cloud Platform has you covered.

For instance, there’s a new 3+ terabyte GitHub Archive hosted on BigQuery, our cloud data warehouse and analytics service. According to the documentation, the archive “contains a full snapshot of the content of more than 2.8 million open source GitHub repositories, including more than 145 million unique commits, over 2 billion different file paths and the contents of the latest revision for 163 million files, all of which are searchable with regular expressions.”

How and what can you learn from this dataset? Google Developer Advocate Francesc Campoy, for one, asks how many Go files are in the GitHub Archive. Six seconds and two billion rows later, BigQuery had an answer: 12,624,178. In the same vein, GitHub asks who has the most commits among .edu contributors. As of a couple of days ago, berkeley.edu led the pack with 816. (Perhaps we can interest Berkeley computer science students with some free GCP credits?)

But the really big news was the undersea FASTER Cable System, which Google and consortium members turned on this week. Google invested $300 million to lay this 9,000km fibre optic cable, which, when fully lit, will carry 10 terabits of data from Oregon and Japan. To put that into context, that’s about 10 million times faster than the average cable modem. Further, FASTER puts the total number of operational, Google-owned undersea cables up to four — four more than any other technology company can lay claim to.