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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Deploying TensorFlow Models on Edge Devices

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…)
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