Machine learning is playing an increasingly important role in many areas of our businesses and our lives and is being employed in a range of computing tasks where programming explicit algorithms is infeasible.
At Amazon, machine learning has been key to many of our business processes, from recommendations to fraud detection, from inventory levels to book classification to abusive review detection. And there are many more application areas where we use machine learning extensively: search, autonomous drones, robotics in fulfillment centers, text and speech recognitions, etc.
Among machine learning algorithms, a class of algorithms called deep learning hascome to represent those algorithms that can absorb huge volumes of data and learn elegant and useful patterns within that data: faces inside photos, the meaning of a text, or the intent of a spoken word. A set of programming models has emerged to help developers define and train AI models with deep learning; along with open source frameworks that put deep learning in the hands of mere mortals. Some examples of popular deep learning frameworks that we support on AWS include Caffe, CNTK, MXNet, TensorFlow, Theano, and Torch.
Among all these popular frameworks, we have concluded that MXNet is the most scalable framework. We believe that the AI community would benefit from putting more effort behind MXNet. Today, we are announcing that MXNet will be our deep learning framework of choice. AWS will contribute code and improved documentation as well as invest in the ecosystem around MXNet. We will partner with other organizations to further advance MXNet.