HOSTKEY deploys a well-established environment for machine learning applications such as neural networks with high-performance GPUs and dedicated servers with NVIDIA GTX 1080/1080Ti and RTX 2080Ti graphics cards. Just start your TensorFlow experience in a straightforward and user-friendly environment making it easy to build, train and deploy machine learning models at scale. TensorFlow runs up to 50% faster on our high-performance GPUs and scales easily. Now your machines learn in hours, not days. Deep Learning is a buzzword that will be familiar to most people.

The steady rise of mobile Internet traffic has provoked a parallel increase in demand for on-device intelligence capabilities. However, the inherent scarcity of resources at the Edge means that satisfying this demand will require creative solutions to old problems. How do you run computationally expensive operations on a device that has limited processing capability without it turning into magma in your hand? The addition of TensorFlow Lite to the TensorFlow ecosystem provides us with the next step forward in machine learning capabilities, allowing us to harness the power of TensorFlow models on mobile and embedded devices while maintaining low latency, efficient runtimes, and accurate inference. TensorFlow Lite provides the framework for a trained TensorFlow model to be compressed and deployed to a mobile or embedded application.

Smart Assistants, fancy image filters in Snapchat and apps like Prisma all have one thing in common--they are powered by Machine Learning. The use of Machine Learning in mobile apps is growing and new mobile apps are developed with Machine Learning based services as business models. In this blog series we want to give you hands-on advice on how you can train and deploy a convolutional neural network for image classification to a mobile app using the popular machine learning framework TensorFlow Mobile. Our task will be to classify images of houseplants which we have collected ourselves. You don't have to go and snap pictures of plants, however, because our approach is generic and can be used for training and deploying a convolutional neural network for image classification, independent of their subject.

TensorFlow Lite is a lightweight and a next step from TensorFlow Mobile. You can do almost all the things that you do on TensorFlow mobile but much faster. Just like TensorFlow Mobile it is majorly focused on the mobile and embedded device developers, so that they can make next level apps on systems like Android, iOS,Raspberry PI etc. It supports a set of core operators, both quantized and float, which have been tuned for mobile platforms. The core operations incorporate pre-fused activations and biases to further enhance performance and quantized accuracy.

TensorFlow Lite for machine learning on mobile devices was first announced by Dave Burke, VP of engineering of Android at the Google I/O 2017. TensorFlow Lite is a lightweight version of Google's TensorFlow open source library that is mainly used for machine learning application by researchers and developers. Now, the search giant has launched the developer preview of a new machine learning toolkit designed specifically for smartphones and embedded devices and will be available for both Android and iOS app developers. This platform will allow developers to deploy AI on mobile devices. It enables on-device machine learning inference with low latency and a small binary size.