According to a 2020 survey of data scientists conducted by Anaconda, data preparation is one of the critical steps in machine learning (ML) and data analytics workflows, and often very time consuming for data scientists. Data scientists spend about 66% of their time on data preparation and analysis tasks, including loading (19%), cleaning (26%), and visualizing data (21%). Amazon SageMaker Studio is the first fully integrated development environment (IDE) for ML. With a single click, data scientists and developers can quickly spin up Studio notebooks to explore datasets and build models. If you prefer a GUI-based and interactive interface, you can use Amazon SageMaker Data Wrangler, with over 300 built in visualizations, analyses, and transformations to efficiently process data backed by Spark without writing a single line of code.

Amazon Web Services claims to have the broadest and most complete set of machine learning capabilities. I honestly don't know how the company can claim those superlatives with a straight face: Yes, the AWS machine learning offerings are broad and fairly complete and rather impressive, but so are those of Google Cloud and Microsoft Azure. Amazon SageMaker Clarify is the new add-on to the Amazon SageMaker machine learning ecosystem for Responsible AI. SageMaker Clarify integrates with SageMaker at three points: in the new Data Wrangler to detect data biases at import time, such as imbalanced classes in the training set, in the Experiments tab of SageMaker Studio to detect biases in the model after training and to explain the importance of features, and in the SageMaker Model Monitor, to detect bias shifts in a deployed model over time. Historically, AWS has presented its services as cloud-only.

Amazon Web Services claims to have the broadest and most complete set of machine learning capabilities. I honestly don't know how the company can claim those superlatives with a straight face: Yes, the AWS machine learning offerings are broad and fairly complete and rather impressive, but so are those of Google Cloud and Microsoft Azure. Amazon SageMaker Clarify is the new add-on to the Amazon SageMaker machine learning ecosystem for Responsible AI. SageMaker Clarify integrates with SageMaker at three points: in the new Data Wrangler to detect data biases at import time, such as imbalanced classes in the training set, in the Experiments tab of SageMaker Studio to detect biases in the model after training and to explain the importance of features, and in the SageMaker Model Monitor, to detect bias shifts in a deployed model over time. Historically, AWS has presented its services as cloud-only.

Nearly three years after it was first launched, Amazon Web Services' SageMaker platform has gotten a significant upgrade in the form of new features making it easier for developers to automate and scale each step of the process to build new automation and machine learning capabilities, the company said. As machine learning moves into the mainstream, business units across organizations will find applications for automation, and AWS is trying to make the development of those bespoke applications easier for its customers. "One of the best parts of having such a widely-adopted service like SageMaker is that we get lots of customer suggestions which fuel our next set of deliverables," said AWS vice president of machine learning, Swami Sivasubramanian. "Today, we are announcing a set of tools for Amazon SageMaker that makes it much easier for developers to build end-to-end machine learning pipelines to prepare, build, train, explain, inspect, monitor, debug and run custom machine learning models with greater visibility, explainability, and automation at scale." Already companies like 3M, ADP, AstraZeneca, Avis, Bayer, Capital One, Cerner, Domino's Pizza, Fidelity Investments, Lenovo, Lyft, T-Mobile, and Thomson Reuters are using SageMaker tools in their own operations, according to AWS.