For software development there are many methodologies, patterns and techniques to build, deploy and run applications. DevOps is the state of the art methodology which describes a software engineering culture with a holistic view of software development and operation. For data science there is a lot of information how machine and deep learning models can be built. The operational aspects seem to still be evolving. I'm currently trying to understand better how to deploy models in the cloud and ...

Early this year IBM announced Deep Learning as a Service within Watson Studio. The core of this service is available as open source and can be run on Kubernetes clusters. This allows developers and data scientists to train models with confidential data on-premises, for example on the Kubernetes-based IBM Cloud Private. (more…)

Building machine and deep learning models from scratch is often not trivial, not for developers and sometimes not even for data scientists. Fortunately over the last years several models have been developed and shared that can be reused and sometimes extended. This allows developers adding AI to applications without having to be data scientists. (more…)

Many developers, including myself, want to use AI in their applications. Building machine learning models however, often requires a lot of expertise and time. This article describes a technique called AutoML which can be used by developers to build models without having to be data scientists. While developers only have to provide the data and define the goals, AutoML figures out the best model automatically. (more…)

While it has been possible to deploy TensorFlow models to mobile and embedded devices via TensorFlow for Mobile for some time, Google released an experimental version of TensorFlow Lite as an evolution of TensorFlow for Mobile at the end of last year. This new functionality allows building exciting AI scenarios on edge devices and the performance of the models is amazing. (more…)

In March IBM launched the Model Asset Exchange which is a place for developers to find and use open source deep learning models. In this article I describe how developers can use these models in applications and how the applications can be deployed to Kubernetes. (more…)

In March IBM announced Deep Learning as a Service (DLaaS) which is part of IBM Watson Studio. Below I describe how to use this service to train models and how to optimize hyperparameters to easily find the best quality model. (more…)

Recently Google introduced TensorFlow.js, which is a JavaScript library for training and deploying machine learning models in browsers and on Node.js. I like especially the ability to run predictions in browsers. Since running this code locally saves the remote calls to servers, the performance is amazing! (more…)

I've continued to play with Unity and the IBM Watson SDK, which allows using cognitive services like speech recognition in Unity projects. With this technology you can not only build games, but also other exciting scenarios. I've changed my Augmented Reality sample slightly to run it as a Virtual Reality app that can be experienced via Google Cardboard and an iPhone. (more…)

Over the last days I've enjoyed playing with Unity and the IBM Watson SDK, which allows using cognitive services like speech recognition in Unity projects. With this technology you can not only build games, but also other exciting scenarios. I've extended an Augmented Reality application from my colleague Amara Keller which allows iOS users to have conversations with a virtual character. (more…)