As you can see in the diagram ... the Amazon SageMaker Studio preview is good enough to use for end-to-end machine learning and deep learning: data preparation, model training, model deployment ...

AWS machine learning service ... You may, however, want to use another algorithm that might work better for your data. Once you have trained your model with Amazon SageMaker, you can test how ...

Previously, we wrote about why we built our Machine ... scientists to use optimized Docker images to speed up their training jobs. Amazon publishes Deep Learning Containers for SageMaker which ...

Pain points and solutions in the machine learning pipeline When Amazon launched SageMaker ... to use: Uber has Michelangelo, Facebook (and likely Instagram and WhatsApp) has FBLearner flow ...

Amazon has announced a new open source project, Neo-AI, which attempts to optimize the performance of machine learning models for a variety of platforms. At re:Invent 2018, AWS added many ...

Amazon SageMaker launched in 2017 for managing the entire machine learning ... model hosting or our SageMaker Model Registry, so customers get a fully managed seamless experience of using MLflow ...

You can’t build a good machine learning model without good training ... Now, however, the company is launching SageMaker Ground Truth, a training set labeling service. Using Ground Truth ...

Over at the All Things Distributed blog, Werner Vogels writes that the new Amazon SageMaker is designed for building ... I think the time is here for using large-scale machine learning in large-scale ...

AWS's Amazon SageMaker ... machine learning algorithms; a "Feature Store" where one can pick out attributes to use in training; and what's known as the Data Wrangler to create original model ...

Amazon Web Services Inc. is lowering the barrier to entry for machine learning ... the best model, then tunes it based on the training data available using Amazon SageMaker Autopilot.