Using Amazon Sagemaker for Scalable Machine Learning Training • Filestack Blog

Not long ago, Amazon unveiled Sagemaker, their machine learning training and deployment infrastructure. To understand why it might be useful, its worth considering the current difficulties of scaling out machine learning services to the cloud. The wild successes of deep learning have increased its demand and taught people to demand its accuracy, which is not easy to achieve without troves of data and the GPU-backed, distributed training platforms to ingest them. A few services out there attempt to help you evade this data barrier: Google ML Engine (and soon AutoML) allow you to train and deploy custom Tensorflow models on Google's cloud infrastructure, Azure has an anaytics platform, BitFusion is trying to help distribute GPUs across cloud providers. There isn't exactly a mad-dash to become the AWS of machine learning, but there is a healthy competition. That being said, there already is an AWS of machine learning: AWS.