Last week at the Google Cloud Next conference, I had the chance to speak about the Java developer’s journey through the “serverless” offering of Google Cloud Platform, with my colleague Vinod Ramachandran (Product Manager on some of our serverless products):

Serverless Java in 2019 is going to be ubiquitous in your favorite cloud. Well, it’s actually been 10 years since you could take advantage of Java on Google App Engine. But now you can run your apps on the brand-new Java 11 runtime. Not only servlet-based apps but also executable JARs. And what about authoring functions? Until now, you could only use Node or Python, but today, Java is the third runtime available for Google Cloud Functions. We will review the various ways you can develop your Java functions. Last but not least, thanks to serverless containers, containerized Java workloads run serverlessly, without you caring for infrastructure, scaling, or paying for idle machines. Through various demos, we will look at the many ways Java developers will be able to write, build, test, and deploy code in Java on the rich serverless offering of Google Cloud Platform.

At GR8Conf Europe last year, I talked  about how to take advantage of the Google Cloud machine learning APIs  using Apache Groovy.

With Groovy, you can call the Vision API that recognises what’s in your pictures, or reads text.

You can invoke the Natural Language API to understand the structure of your text.

With the Speech-To-Text API, you can get transcriptions of what’s been said in an audio stream, or with Text-To-Spech, you can also generate human-like voices from your own text.

At Voxxed Days Microservices, in Paris, I talked about the latest development in serverless solutions on Google Cloud Platform, to deploy functions, apps  and even containers.

I answered an interview  on the theme of microservices, and how this maps to the Google cloud products.

And the video of my presentation was published on YouTube:

Here’s the abstract of the session:

In a matter of a few years, Google Cloud Platform has evolved from a very small set of products or APIs to a wealth of close to a hundred of products, services and APIs that developers can take advantage of.

This week, at the event Le Meilleur Dev de France, I gave an introduction to the whole platform, focusing on three key axis: compute, storage and machine learning. After an introduction on famous users of GCP, like Snapchat, Spotify or PokemonGo, I also gave a few examples of big French companies as well as French startups who have decided to go to the cloud with Google.

A coupe weeks ago, I had the chance to talk at Cloud Next 2018, in San Francisco, with my colleague and friend Alexis. We talked about building and deploying microservices with Google App Engine and Cloud Functions. I’ve been a big fan of App Engine since 2009 when Google released the Java flavor, and have been enjoying doing a bit of Node / JavaScript on Cloud Functions since it came in beta. So I was very happy to be able to talk about those two serverless solutions.

Last month in sunny Napa valley, my awesome colleague Seth Vargo and I had the chance to speak at SwampUp, the devops focused conference organized by JFrog. Our talk & demo were focused on the topic of “ChatOps”. But what is ChatOps? Here’s what our abstract said:

Heard of ChatOps? It’s a movement in the DevOps community to take advantage of Chatbots. 

Chatbots centralize the conversation and history of your daily operations including build status, issue management, deployment, and monitoring, so that you access all the information and actions needed at the whim of a chat message in your team communication solution.

Read more...

My buddy Wassim and I were back on stage together to talk about chatbots, with Actions on Google and Dialogflow, at DevFest Lille and Best of Web Paris. I’d like to share with you the slides of the presentation (the video has been recorded and will be available at a later time.)

You might be interested in those two codelabs to get started on this journey:

• Build Actions for the Google Assistant - level 1
• Build Actions for the Google Assistant - level 2

Here’s the presentation given at DevFest Lille:

Last March, I had the chance to attend and speak at QCon London. I spoke at the event for its first edition, many moons prior, so it was fun coming back and seeing how the conference evolved. 

This year, Eric Horesnyi of Streamdata  was leading the Artificial Intelligence track, and invited me to speak about Machine Learning.

First, I gave an overview of the Machine Learning offering, from the off-the-shelf ready-made APIs like  Vision, Speech,  Natural Language, Video Intelligence. I also mentioned AutoML, to further train existing models like the Vision model in order to recognize your own specific details in pictures. For chatbots, I also covered Dialogflow. And I said a few words about Tensorflow and Cloud Machine Learning Engine  for training & running your Tensorflow models in the cloud. You can watch the video by clicking on the picture below:

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:

What can you learn from millions of (Groovy) source files stored on Github? In this presentation, I analized source files in the Github archives stored on BigQuery, and in particular Groovy source file, but also Gradle build files, or Grails controllers and services.

What kind of questions can we answer

• How many Groovy files are there on Github?
• What are the most popular Groovy file names?
• How many lines of Groovy source code are there?
• What’s the distribution of size of source files?
• What are the most frequent imported packages?
• What are the most popular Groovy APIs used?
• What are the most used AST transformations?
• Do people use import aliases much?
• Did developers adopt traits?

For Gradle, here are the questions that I answered: