Pictures from W-JAX 2018

This week I had the pleasure to attend W-JAX in Munich which is a conference for Java and enterprise developers with 1400 attendees. I've been to JAX and W-JAX several times and really like this conference. Great speakers, great content, great discussions. Below are some pictures. (more…)
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Pictures from data2day

This week my colleagues Stephan Reimann and Thomas S├╝dbroecker and I attended data2day in Heidelberg. data2day is a technical conference for data scientists and developers focussed on topics related to big data, artificial intelligence and machine learning. Below are some pictures, most of them taken by Thomas. (more…)
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Training TensorFlow Object Detection Models

TensorFlow Object Detection is a powerful technology to recognize different objects in images including their positions. The trained Object Detection models can be run on mobile and edge devices to execute predictions really fast. I've used this technology to build a demo where Anki Overdrive cars and obstacles are detected via an iOS app. When obstacles are detected, the cars are stopped automatically. (more…)
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Deploying Machine Learning Models in the Cloud

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 ...
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Training AI Models on Kubernetes

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…)
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Reusing Open Source Models in AI Applications

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…)
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Building Models with AutoML in IBM Watson Studio

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…)
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