Large Language Models (LLMs for short) are taking the world by storm, and things like ChatGPT have become very popular and used by millions of users daily. Google came up with its own chatbot called Bard, which is powered by its ground-breaking PaLM 2 model and API. You can also find and use the PaLM API from withing Google Cloud as well (as part of Vertex AI Generative AI products) and thus create your own applications based on that API. However, if you look at the documentation, you’ll only find Python tutorials or notebooks, or also explanations on how to make cURL calls to the API. But since I’m a Java (and Groovy) developer at heart, I was interested in seeing how to do this from the Java world.

At enterprises across industries, documents are at the center of core business processes. Documents store a treasure trove of valuable information whether it’s a company’s invoices, HR documents, tax forms and much more. However, the unstructured nature of documents make them difficult to work with as a data source. We call this “dark data” or unstructured data that businesses collect, process and store but do not utilize for purposes such as analytics, monetization, etc. These documents in pdf or image formats, often trigger complex processes that have historically relied on fragmented technology and manual steps. With compute solutions on Google Cloud and Document AI, you can create seamless integrations and easy to use applications for your users. Document AI is a platform and a family of solutions that help businesses to transform documents into structured data backed by machine learning. In this blog post we’ll walk you through how to use Serverless technology to process documents with Cloud Functions, and with workflows of business processes orchestrating microservices, API calls, and functions, thanks to Workflows.

I missed this talk from Alexandre Dubreuil, when attending Devoxx Belgium 2019, but I had the chance to watch while doing my elliptical bike run, confined at home. It’s about applying Machine Learning to music generation, thanks to the Magenta project, which is based on Tensorflow.

I like playing music (a bit of piano & guitar) once in a while, so as a geek, I’ve also always been interested in computer generated music. And it’s hard to generate music that actually sounds pleasant to the ear! Alexandre explains that it’s hard to encode the rules a computer could follow to play music, but that machine learning is pretty interesting, as it’s able to learn complex functions, thus understanding what does sound good.

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.

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:

Powerful machine learning APIs are at your fingertips if you’re developing with Google Cloud Platform, as client libraries are available for various programming languages. Today, we’re investigating the Cloud Vision API and its Java SDK, using the Apache Groovy programming language—a multi-faceted language for the Java platform that aims to improve developer productivity thanks to a concise, familiar and easy to learn syntax.

At GR8Conf Europe, in Denmark, the conference dedicated to the Apache Groovy ecosystem, I spoke about the machine learning APIs provided by Google Cloud Platform: Vision, Natural Language, Translate, and Speech (both recognition and synthesis). Since it’s a groovy conference, we presented samples and demos using a pretty Groovy language. I wanted to share the underlying examples with a wider audience, so here’s the first of a series of blog posts covering the demos I presented. I’ll start with the Google Cloud Vision API, and I will cover the other APIs in future posts.

Last week at GR8Conf Europe, I spoke about the machine learning APIs provided by  Google Cloud Platform: Vision, Natural Language, Speech recognition and synthesis, etc. Since it’s GR8Conf, that means showing samples and demos using a pretty Groovy language, and I promised to share my code afterwards. So here’s a series of blog posts covering the demos I’ve presented. We’ll start with the Vision API.

The Vision API allows you to:

• Get labels of what appears in your pictures,
• Detect faces, with precise location of face features,
• Tell you if the picture is a particular landmark,
• Check for inappropriate content,
• Give you some image attributes information,
• Find if the picture is already available on the net,
• Detects brand logos,
• Or extract text that appears in your images (OCR).

You can try out those features online directly from the Cloud Vision API product page:

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:

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.