Today was announced the general availability of Google Cloud Endpoints!

Endpoints is the Google Cloud Platform solution for Web API management, which lets you easily protect & secure your API, monitor it, without overhead, and even allows you to implement your API with any language or framework you want.

I’ve spoken about Endpoints a few times already, at Devoxx Belgium, Nordic APIs summit, and APIDays Paris. And you can see the recording of my Nordic APIs appearance, if you want to learn more about Cloud Endpoints:

For Google Cloud Next and Devoxx France, I’m working on a new talk showing how to build a conference assistant, to whom you’ll be able to ask questions like “what is the next talk about Java”, “when is Guillaume Laforge speaking”, “what is the topic of the ongoing keynote”, etc.

For that purpose, I’m developing the assistant using API.AI. It’s a “conversational user experience platform” recently acquired by Google, which allows you to define various “intents” which correspond to the kind of questions / sentences that a user can say, and various “entities” which relate to the concepts dealt with (in my example, I have entities like “talk” or “speaker”). API.AI lets you define sentences pretty much in free form, and it derives what must be the various entities in the sentences, and is able to actually understand more sentences that you’ve given it. Pretty clever machine learning and natural language process at play. In addition to that, you also have support for several spoken languages (English, French, Italian, Chinese and more), integrations with key messaging platforms like Slack, Facebook Messenger, Twilio, or Google Home. It also offers various SDKs so you can integrate it easily in your website, mobile application, backend code (Java, Android, Node, C#…)

The schedule for Google Cloud Next was unveiled this week, and there’s lots of interesting sessions to attend. With the many parallel tracks, it’s difficult to make a choice, but I wanted to highlight some of the talks I’d like to watch!

The Google Cloud Platform is a pretty rich one, with many options for your compute needs. How do you choose which one is best for your use case? Brian Dorsey covers this in detail in this session:

The purpose of this article is to deploy a Ratpack web application on Google App Engine Flex.

For my demos at conferences, I often use frameworks like Ratpack, Grails or Gaelyk, which are based on the Apache Groovy programming language. In a previous article, I already used Ratpack, but on a slightly more complex use case, but this time, I want to share a quick Ratpack hello world, and deploy it on Flex.

I started with a hello world template generated by Lazybones (a simple project creation tool that uses packaged project templates), that I had installed with SDKman (a tool for managing parallel versions of multiple Software Development Kits). But you can go ahead with your own Ratpack apps obviously. Feel free to skip the next section if you already have an app.

The GA release of Cloud Natural Language API is easier to use, better at recognizing language nuances and adds additional support for Spanish and Japanese

Earlier in November, we announced general availability for the Cloud Natural Language API and highlighted the key new improvements. This launch included many additions to the API like expanded entity recognition, granular sentiment analysis with expanded language support, improved syntax analysis with additional morphologies and more.

Many of these improvements were the result of feedback from beta users, so thank you for your contributions! But concretely, what do these updates mean? Let’s take a closer look.

All sorts of voice-powered assistants are available today, and chat bots are the new black! In order to illustrate how such tools are made, I decided to create my own little basic conference assistant, using Google’s Cloud Speech API and Cloud Natural Language API. This is a demo I actually created for the Devoxx 2016 keynote, when Stephan Janssen invited me on stage to speak about Machine Learning. And to make this demo more fun, I implemented it with a shell script, some curl calls, plus some other handy command-line tools.

With my colleague Martin Görner, at the Devoxx conference in Belgium last month, we gave a talk on Machine Learning, on the various APIs provided by Google Cloud, the TensorFlow Machine Learning Open Source project, the Cloud ML service. I didn’t get a chance to publish the slides, so it’s time I fix that!

Machine Intelligence at Google Scale: Vision/Speech API, TensorFlow and Cloud Machine Learning

The biggest challenge of Deep Learning technology is the scalability. As long as using single GPU server, you have to wait for hours or days to get the result of your work. This doesn’t scale for production service, so you need a Distributed Training on the cloud eventually. Google has been building infrastructure for training the large scale neural network on the cloud for years, and now started to share the technology with external developers. In this session, we will introduce new pre-trained ML services such as Cloud Vision API and Speech API that works without any training. Also, we will look how TensorFlow and Cloud Machine Learning will accelerate custom model training for 10x - 40x with Google’s distributed training infrastructure.

Gradle is becoming the build automation solution of choice among developers, in particular in the Java ecosystem. With the Github archive published as a Google BigQuery dataset, it’s possible to analyze those build files, and see if we can learn something interesting about them!

This week, I was at the G3 Summit conference, and presented about this topic: I covered the Apache Groovy language, as per my previous article, but I expanded my queries to also look at Grails applications, and Gradle build files. So let’s see what the dataset tells us about Gradle!

At Devoxx last week, I had the chance to do a joint tools-in-action with my talented friend Audrey Neveu, titled Binge streaming you Web API:

In a fast-paced fashion, to keep you awake after long University sessions, Audrey and Guillaume will set you up to create a Web API using Google Cloud Endpoints, and stream the content of the API in real-time with Streamdata.io. After a quick introduction to both technologies, they’ll build together both the backend and the front-end to interact live with the audience, through the Web or via a mobile app.

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When you’re following a project, a company, a platform, you’re looking for the latest news, about the latest feature announcement, to take advantage of what’s coming up.

Last time, I blogged about the gcloud command line tool, which nicely shows you the latest updates since the last time you updated its components.

If you go to the Google Cloud Platform website, you’ll see dedicated release notes pages for pretty much all products. For example, here are the release notes for: