Build, train, and deploy your ML models fasterwith Amazon SageMaker

Amazon SageMaker comes with automated hyperparameter optimization (HPO), adjusting thousands of different combinations of algorithm parameters, to arrive at the most accurate predictions the model is capable of producing.

Amazon SageMaker supports all frameworks and comes pre-configured with TensorFlow and Apache MXNet. If you want to train with an alternative framework, you can bring your own in a Docker container.

Amazon SageMaker provides discrete modules for authoring, training, and hosting models so that you can choose to keep the parts of your existing workflows that you like, and insert Amazon SageMaker into the parts you don’t.

Amazon SageMaker provides built-in algorithms like K-Means Clustering, ResNet, and Factorization Machines. But, they’ve been optimized for large datasets to perform two- to ten-times faster than what can be achieved anywhere else.

Amazon SageMaker provides a library of Juptyer notebooks with sample code for a wide variety of machine learning and deep learning projects to help you get new projects started quickly.

Training on Amazon SageMaker can automatically distribute processing across an infinite number of nodes - including P3 instances built on NVIDIA V100 GPUs – and since training environments live only as long as the job, you pay only for what you use.

With Amazon SageMaker, you can deploy your model into production without making application code changes. It also allows for automatic A/B testing of models and handles all of the routine maintenance and monitoring of your production model servers.