It’s a great time to start planning your infrastructure project to move to the cloud or if you starting a business you could just avoid the hassle to setup and manage your internal infrastructure by adopting a cloud solution.
For you that already embrace the idea of the cloud and see the benefits, it’s a marvelous time, and when more conservative industries like banks are becoming tech companies, you know that there is a big shift going on and you better get educated and make plans for your own shift.
Last week, I attended Google Next 17 in Amsterdam and it was amazing how the different Google services has come together to an enterprise offering. The enterprise offering goes under the name Google Cloud and the products in the Google Cloud are G Suite, Google Cloud Platform, Map, Chrome, Android and what exacts me Machine learning API’s
With the Google Cloud product bundle, Google is now ready to compete with Amazon Web Service (AWS), MicroSoft (Azure), IBM (BlueMix).
At the Google Next 17 there was many announcements, here are some:
- Vault for Google Drive – eDiscovery and archiving solution for G Suite
- Microsoft SQL Server Enterprise – Available on Google Compute Engine, Support for Windows Server Failover
- Machine Learning API’s
- One-click Kubernetes clusters, managed by Google
- Team Drives and Drive File Stream
- Hangouts Meet & Hangouts Chat
- End to End Cloud Platform Security
- Open Source
- Build natural languages bots – API.AI
I was most excited about the API’s, I been working with Alchemyapi now part of IBM Bluemix in past projects, especially around Natural Language. I can not wait to try out Google’s new machine learning APIs. With the launch of Machine Learning APis Major Google applications use Cloud Machine Learning, including Photos (image search), the Google app (voice search), Translate, and Inbox (Smart Reply). Google Machine Learning APIs:
Google Cloud Video Intelligence API makes videos searchable, and discoverable, by extracting metadata with an easy to use REST API. You can now search every moment of every video file in your catalog and find every occurrence as well as its significance.
Google Cloud Vision API – enables developers to understand the content of an image by encapsulating powerful machine learning models. It quickly classifies images into thousands of categories (e.g., “cat”, “women”, “Museum”), detects individual objects and faces within images, and finds and reads printed words contained within images.
Google Cloud Speech API – enables developers to convert audio to text by applying powerful neural network models in an easy to use API.
Google Cloud Natural Language API – reveals the structure and meaning of text by offering powerful machine learning models. You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your campaigns on social media.
Cloud Translation API – provides a simple programmatic interface for translating an arbitrary string into any supported language using state-of-the-art Neural Machine Translation.
One of the cornerstones of the Google Cloud is security, what currently stands Google from their competitors are that Google controls all aspects of a data centre infrastructure, they build their own cpu’s, their own servers, they operate their own private fiber optic network, which allows them to secure their data centers using machine learning to protect the network.
Here is a Google Diagram over the various security actions by layers:
Google embraces open source in many of its platforms, to take an example Google offer Kubernetes in the Google Cloud Platform, Kubernetes open source offering was not possible without the Open Source community. You can find a list of other open source projects here.
API.AI provides tools to developers to help them build conversational bots. Acquired by Google, API.AI handle things like speech recognition, intent recognition, and context management, and allows devs to provide domain-specific knowledge (like that “deep dish” and “Chicago-style” can probably mean the same thing to your pizza delivery bot) that might be unique to your bot’s needs.
At Google NEXT 17 that I attended in Amsterdam, the attendance came from various enterprises from near and far. Talking and listening to conversations you realize it’s not going to be a swift transition for enterprise to go from owned infrastructure to cloud infrastructure. A few things that I heard that needs to be overcome:
- Not Invented Here Syndrome – You need to give ownership of your cloud-based project to your IT staff. Your IT staff developed your legacy systems over many years in response to their particular needs and your IT staff feel an emotional attachment to it.
- Loss of Control – SysAdmins can feel they’ve been demoted to users on a Cloud system they don’t control or own at a physical level. I the Cloud they may not have direct access to the hardware, but they will still be very much in control of the system.
- Data Security – Compliance and governance strike fear into executives’ hearts and are a serious barrier to Cloud adoption. Once an organization has a policy in place and an in-house system that satisfies the regulations, change is difficult.
- Data Privacy – There is a concern about who will have access to an organization’s data if it is processed in a Cloud. Google and other cloud providers conform to industry security practices, security audits and they obtain certifications for the jurisdiction they offer their services.
- Cost – With Cloud services, the services need to be paid for every year, whereas traditional in-house provision is an investment that can be amortized over many years. Start small moving to the cloud so the benefits of the cost model in the cloud are clear to all parties in your organization. Let them see it makes sense.
I hoped this gave you an update on what I learned from Google NEXT. If you are up to it, leave a comment below and let’s get a dialogue going.