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.

Last week, I was in San Francisco for my tenth JavaOne! I had two sessions: one on the past / present / future of Java Platform-as-a-Service offerings, and one on programming language influences, and particularly how was Apache Groovy influenced, and how it also inspired other languages.

Here’s the abstract:

Languages have been influencing one another since the dawn of computer programming. There are families of languages: from Algol descendants with begin/end code blocks to those with curly braces such as C. Languages are not invented in a vacuum but are inspired by their predecessors. This session’s speaker, who has been working on Apache Groovy for the past 14 years, reflects on the influences that have driven the design of programming languages. In particular, Groovy’s base syntax was directly derived from Java’s but quickly developed its own flavor, adding closures, type inference, and operators from Ruby. Groovy also inspired other languages: C#, Swift, and JavaScript adopted Groovy’s null-safe navigation operator and the famous Elvis operator.

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A few weeks ago, my buddy Wassim and I had the chance to present again on the topic of chatbots, with API.AI and Cloud Functions, at the DevFest Toulouse conference.

Here’s the latest update to our slide deck:

There’s also a video (in French) of the same content from Devoxx France, where I was showing how to build a conference chatbot:

Chatbots, per se, are not really new, in the sense that we’ve been developing bots for things like IRC for a long time, but back in the day, it was simply some regular expression labor of love, rather than the natural language that we use today. The progress in machine learning, in both speech recognition (for when you use devices like Google Home) and natural language understanding (NLU), is what led us to being able to speak and chat naturally to those chatbots we encounter now.

Last week, I had the pleasure to speak at the Jenkins Community Day conference, in Paris, organized by my friends from JFrog, provider of awesome tools for software management and distribution. I covered how to scale Jenkins with Kubernetes on Google Container Engine.

For the impatient, here are the slides of the presentation I’ve given:

But let’s step back a little. In this article, I’d like to share with you why you would want to run Jenkins in the cloud, as well as give you some pointers to interesting resources on the topic.

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!

That’s Devoxx France this week, and I’ve had the pleasure of delivering today another talk on the theme of chatbots, using Cloud Functions for the business logic, API.AI for the bot cleverness, with a bonus of a demo through Google Home and the Google Assistant platform.

I’ll post the YouTube video recording once it’s online, but in the meantime, I wanted to share my slides here:

And the video in French:

Last week, in San Francisco, took place the Google Cloud Next 2017 conference, and I had the pleasure to co-present a session on “Extending the Google Assistant with Actions on Google”, with Brad Abrams, product manager on the assistant technology at Google.

The Google Assistant is the conversational user interface that helps you get things done in your world. Actions on Google let you build on this assistance, while your integrations can help you engage users through Google Home on Pixel, Android and many other devices that connect with Google Assistant. In this session, we’ll share the latest innovations behind the Google Assistant and how you can leverage those technologies and best practices for Voice User Interface design to build your own custom extensions to Google Assistant.

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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.

This week, I’m in Florida for the brand new G3 Summit conference, dedicated to the Apache Groovy ecosystem (Grails, Gradle, and more). I had the chance of giving the keynote, where I gave an overview of the Apache Groovy project’s philosophy, history, and where it’s heading. In the second part, I’m showcasing the new features, new syntax constructs, already there or coming in Groovy 2.4.x, in the future Groovy 2.5, and in Groovy 3.0 with the new parser.

When I joined Google last June, I discovered a new world: tons of new acronyms or project code names to learn about, but also a particular environment for your source code. At Google, engineers work on a huge monolithic source code repository comprising of: 

• 1 billion files
• 9 million source files
• 2 billion lines of code
• 35 million commits
• 86 terabytes of content
• 45 thousands of commits every day.

Rachel Potvin, who’s an engineering manager at Google, wrote an article for ACM about how Google handles such a huge repository, as well as the tools and practices around that. Wired also covered the topic in their article “Google is 2 billion lines of code and it’s all in one place”. And Rachel also presented this topic at the @Scale conference.