In addition, Amazon pushed out new features that automatically compile training code to maximize compute and memory usage, help ML engineers choose the most appropriate instances to test models and enable unification of data processing and machine learning workloads for easier collaboration among data engineers and data scientists. Several services have provided often low-paid data labeling labor for years. The company’s human “expert labelers” categorize information in datasets, then at some point as its ML systems learn from that labeled data, their automated processes take over to “pre-label” data like images. But unlike its other automated offerings, its Ground Truth Plus service does require human intervention in a hybrid process. The system also provides reports and explanations of how the models work.Īnother service aimed at putting machine learning in the hands of non-practitioners and students is SageMaker Studio Lab, a tool that enables free access to AWS compute resources so people can experiment with ML models and store projects to come back to later.Īmazon also aims to ease and speed up some of the tedious backend work required to prepare data used to train machine learning algorithms.

The tool promises to help even data novices choose training data and pick the most appropriate ML models without writing any code. The company said its no-code SageMaker Canvas tool, which uses a drag-and-drop interface to walk people through an ML workflow, is now widely available.

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