Favorite As Artificial Intelligence (AI) and Machine Learning (ML) technologies have become mainstream, many enterprises have been successful in building critical business applications powered by ML models at scale in production. However, since these ML models are making critical business decisions for the business, it’s important for enterprises to add

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Shared by AWS Machine Learning August 31, 2023

Favorite Radiology reports are comprehensive, lengthy documents that describe and interpret the results of a radiological imaging examination. In a typical workflow, the radiologist supervises, reads, and interprets the images, and then concisely summarizes the key findings. The summarization (or impression) is the most important part of the report because

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Shared by AWS Machine Learning August 30, 2023

Favorite Maintaining machine learning (ML) workflows in production is a challenging task because it requires creating continuous integration and continuous delivery (CI/CD) pipelines for ML code and models, model versioning, monitoring for data and concept drift, model retraining, and a manual approval process to ensure new versions of the model

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Shared by AWS Machine Learning August 29, 2023

Favorite As part of the 2023 Data Science Conference (DSCO 23), AWS partnered with the Data Institute at the University of San Francisco (USF) to conduct a datathon. Participants, both high school and undergraduate students, competed on a data science project that focused on air quality and sustainability. The Data

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Shared by AWS Machine Learning August 28, 2023

Favorite Today, we’re pleased to announce the preview of Amazon SageMaker Profiler, a capability of Amazon SageMaker that provides a detailed view into the AWS compute resources provisioned during training deep learning models on SageMaker. With SageMaker Profiler, you can track all activities on CPUs and GPUs, such as CPU

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Shared by AWS Machine Learning August 25, 2023

Favorite We’re excited to announce Amazon SageMaker Data Wrangler support for Amazon S3 Access Points. With its visual point and click interface, SageMaker Data Wrangler simplifies the process of data preparation and feature engineering including data selection, cleansing, exploration, and visualization, while S3 Access Points simplifies data access by providing

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Shared by AWS Machine Learning August 23, 2023

Favorite Amazon CodeWhisperer, the AWS AI coding companion, is a step change in developer productivity tools. Based on generative AI technology, Amazon CodeWhisperer offers contextualized code snippets or recommendations based on natural language prompts to build software quickly, responsibly, and securely. It enables productivity gains and increases accuracy for accelerated

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Shared by AWS Machine Learning August 23, 2023

Favorite Explainability of machine learning (ML) models used in the medical domain is becoming increasingly important because models need to be explained from a number of perspectives in order to gain adoption. These perspectives range from medical, technological, legal, and the most important perspective—the patient’s. Models developed on text in

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Shared by AWS Machine Learning August 22, 2023

Favorite Machine learning (ML) is revolutionizing solutions across industries and driving new forms of insights and intelligence from data. Many ML algorithms train over large datasets, generalizing patterns it finds in the data and inferring results from those patterns as new unseen records are processed. Usually, if the dataset or

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Shared by AWS Machine Learning August 22, 2023

Favorite Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection,

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Shared by AWS Machine Learning August 21, 2023