Back to GR8Conf Europe in Denmark, for the yearly Groovy community reunion! I had the chance to present two talks.

The first one on Google’s Machine Learning APIs, with samples in Groovy using vision recognition, speech recognition & generation, natural language analysis. I’ll come back on ML in Groovy in forthcoming articles.

And the second talk was an overview of Google Cloud Platform, focusing on the compute and storage options, with demos using Groovy frameworks (Ratpack, Gaelyk, and the newly released Micronaut) and how to deploy apps on Compute Engine, Kubernetes Engine, App Engine. I’ll also come back in further articles on those demos, but in the meantime, I wanted to share my slide deck with you all! Without further ado, here’s what I presented:

Last month, for the first time, I visited Riga (Latvia), for the DevTernity conference. I really enjoyed my time there, and wish to come back with other topics next time. The organizers took very well care of the speakers, and the presentations were very interesting.

I had the pleasure to talk about the pre-trained machine learning APIs provided by Google Cloud Platform, and say a few words as well about TensorFlow and Cloud ML Engine.

In two weeks, I’ll be flying east, much further east than I’ve ever been! I’ll visit Singapore! And I’ll have a pretty busy week with several events: conference, meetup, user groups, brown bag lunch… and I’ll talk about Groovy, Machine Learning, and chatbots!

First of all, on Wednesday 31st, I’ll participate to the Singapore Java User Group, where I’ll give an update on Apache Groovy (the latest improvements, new features, the roadmap).

The JAX conference, in Mainz, Germany, is coming to an end. I was there with my colleagues Mete and Robert, and had the chance to cover two topics: Machine Learning and Scaling Web APIs. It’s a pleasure to come back to this conference where the audience is always very focused, eager to learn, and is engaging in great and interesting conversations.

Machine Intelligence at Google Scale

My first presentation was about Machine Learning, and in particular with the Google Cloud APIs, including Vision, Speech, Natural Language, Translate, and Video Intelligence. Although I’m not an expert in TensorFlow and the Cloud Machine Learning Engine, I got a chance to say a few words about these. I guess I’ll have to play with both at some point to be able to tell even more!

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.