At Next ’17 Google announced 100 new products, partnerships, and services at the three-day event. A few of my highlights are below. They are a mix of G Suite related as well as Cloud services-related things that are geared to what we’re doing with machine learning and artificial intelligence efforts over at our sister company, XVA Labs. Here’s my download—
Quick Access in Drive General Availability (Google Suite Apps)
The selling point here is the ability to surface the right information at the right time within Google Drive. Quick Access in Drive now works on Android and iOS.
Hangouts Meet Google Suite Apps
Meet lets up to 30 participants join a video call without the need for plugins, downloads, or accounts. Meet works with a fast, lightweight interface and smart participant management—just set up a meeting and share a link.
Hangouts Chat Early Access Program (EAP)
Connect cross-functional enterprise teams by communicating in Hangouts Chat’s dedicated permanent rooms. Chat integrates with G Suite Apps like Drive and other third-party enterprise apps. Part of the functionality of Hangouts Chat is @meet, a bot that works with Calendar to automate scheduling meetings. This is a bit confusing, with the whole Hangouts Meet terminology. Just think of Meet as the intelligence that does all the coordinating for all the different people who want to join a Hangout. But forget about @meet, the keyword here is ‘Permanent’. So, kind of like a Slack metaphor I suppose.
Gmail Add-ons for G Suite (Developer Preview)
With Add-ons, developers can build a Gmail integration once and have it run natively in Gmail across web, Android, and iOS. End-users can expect Add-ons for popular third-party apps and services to be made available through the G Suite Marketplace by the end of this year.
Jamboard [pumped about this one!]
This collaborative digital whiteboard makes it easy for your team to brainstorm and create within or beyond the walls of your company. You can project Jamboard into Hangouts, participate from your tablet or phone, and pull in content from the rest of G Suite. Its 55˝ 4k display offers best-in-class touch response time and will be available this May.
FOR G SUITE ENTERPRISE, BUSINESS, EDUCATION CUSTOMERS
Team Drives for G Suite GA
Now available for all G Suite customers, Team Drives help teams simply and securely manage file permissions, ownership, and access for an organization within Google Drive. Google Vault and Quick Access are also included.
Drive File Stream (EAP)
This is long overdue, and huge for those of us working with kludged together /mounts. Employees can now access cloud storage content directly from their desktops, without requiring a sync or monopolizing hard drive space.
And the Kaggle Acquisition: Ohboyohboyohboy
There was also a huge acquisition announcement with Google’s purchase of Kaggle, and sometimes big deals come in small packages. Kaggle is one of the world’s largest communities of data scientists and machine learning enthusiasts. Talk about some serious brains.
Kaggle and Google Cloud will continue to support machine learning training and deployment services, in addition to offering the community the ability to store and query large datasets.
It is super smart to be integrating and packaging big commercial datasets right now. Businesses often look for datasets (public or commercial) outside their organizational boundaries. Commercial datasets offered include financial market data from Xignite, residential real-estate valuations (historical and projected) from HouseCanary, predictions for when a house will go on sale from Remine, historical weather data from AccuWeather, and news archives from Dow Jones, all immediately ready for use in BigQuery (with more to come as new partners join the program).
I know. It sounds like nothing but geek talk, but this is a big deal. The vast majority of us in machine learning and natural language processing use Python as our language of choice for a variety of reasons. So may I present Python for Google Cloud Dataflow in GA! Cloud Dataflow is a fully managed data processing service supporting both batch and stream execution of pipelines. Until recently, these benefits have been available solely to Java developers. Now there’s a Python SDK for Cloud Dataflow in GA. Yes, it’s a geek’s wet dream, not gonna lie.
Cloud Machine Learning Engine (G Suite Apps)
Cloud Machine Learning Engine, now generally available, is for organizations that want to train and deploy their own models into production in the cloud. I don’t currently have a ton of business use for this one but it’s certainly very cool to play with. Basically, imagine being able to search for video based upon what is *in* the video without anyone having had to add tons of descriptive metadata. If there’s a flower in the video, it figured that out itself versus being told. This includes determining complex visual motions and defining a sort of searchable narrative. It’s a big deal.
Cloud Video Intelligence API (Private Beta).
A first of its kind, Cloud Video Intelligence API lets developers easily search and discover video content by providing information about entities (nouns such as “dog,” “flower,” or “human” or verbs such as “run,” “swim,” or “fly”) inside video content. With video content creation pretty much exploding, this functionality is pretty cool.
Other Random Info
To better compete with Amazon and AWS Google has extended their free trial to 12 months from 60 days, allowing you to use your $300 credit across all GCP services and APIs, at your own pace and schedule. With all the competition in the collaboration space right now, that’s smart.
They’ve also introduced new Always Free products, which are non-expiring usage limits that you can use to test and develop applications at no cost. Visit the Google Cloud Platform Free Tier page for details.
Coursera just keeps proving to me it’s the leader in next gen education. My Machine Learning certification at Stanford was handled through Coursera and was great. Coursera announced that it is collaborating with Google Cloud Platform to provide an extensive range of Google Cloud training courses.
DocuSign announced tighter integration into Google Docs so perhaps I’ll finally have single point contract workflow with Docs and actually use it more.
We’ll see what they look like but there are supposedly some cool toolkits coming out for TensorFlow as part of the Intel / Google partnership. TensorFlow is amazing so I’m trying to temper myself until I see what’s what.
Partner specializations were announced and, while definitely useful for establishing credibility, I honestly see this as part of Google’s talent acquisition strategy in machine learning and AI. But hey, that’s a win/win. Partners demonstrating strong customer success and technical proficiency in certain solution areas will now qualify to apply for a specialization. We’re launching specializations in application development, data analytics, machine learning, and infrastructure.
After all of that? I’m just more torn than ever when it comes to what cloud platform to develop and house big data, machine learning, and NLP applications. They’ve certainly closed some of the holes that existed between them and Amazon and IBM (no, Microsoft hasn’t made my shortlist). But the winner in this particular war won’t simply be capabilities—it’ll be who can onboard across various skill sets most effectively. Surprisingly I think IBM, with its slightly less diluted offering and very usable out of the box APIs is winning that battle in the places it goes head to head. But of course, time will tell on that front. It always does.
That said? Why you wouldn’t be running your traditional business comms and auth structure on GSuite at this point is beyond me, and given that, if you’re developing internal applications—Google Cloud services has a major advantage. IBM and Microsoft, whether cloud or on-premise, are still very top heavy for deployment and maintenance in comparison. Okay, that’s my download. What’ve you got?
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A proven, entrepreneurial minded senior executive. While Matt is a well known thought leader, speaker, writer & evangelist these are simply artifacts of having rolled up his sleeves & ‘done the work’. He works from a position of direct knowledge through execution & not feel-good theory.
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