AWS Machine Learning Blog

For the past few decades, physician burnout has been a challenge in the healthcare industry. Although patient interaction and diagnosis are critical aspects of a physician’s job, administrative tasks are equally taxing and time-consuming. Physicians and clinicians must keep a detailed medical record for each patient. That record is stored in the hospital electronic health […]

This is a guest post authored by Paul Ngo, US Gas Technical and Operational Services Data Team Lead at TC Energy. TC Energy operates a network of pipelines, including 57,900 miles of natural gas and 3,000 miles of oil and liquid pipelines, throughout North America. TC Energy enables a stable network of natural gas and […]

For a supervised machine learning (ML) problem, labels are values expected to be learned and predicted by a model. To obtain accurate labels, ML practitioners can either record them in real time or conduct offline data annotation, which are activities that assign labels to the dataset based on human intelligence. However, manual dataset annotation can […]

August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. Cities across the world are transforming their public services infrastructure with the mission of enhancing the quality of life of its residents. Roads and traffic management systems […]

Feature engineering is a process of applying transformations on raw data that a machine learning (ML) model can use. As an organization scales, this process is typically repeated by multiple teams that use the same features for different ML solutions. Because of this, organizations are forced to develop their own feature management system. Additionally, you […]

You might use traditional methods to forecast future business outcomes, but these traditional methods are often not flexible enough to account for varying factors, such as weather or promotions, outside of the traditional time series data considered. With the advancement of machine learning (ML) and the elasticity that the AWS Cloud brings, you can now […]

Have you noticed that your shopping preferences are influenced by the weather? For example, on hot days would you rather drink a lemonade vs. a hot coffee? Customers from consumer-packaged goods (CPG) and retail industries wanted to better understand how weather conditions like temperature and rain can be used to provide better purchase suggestions to […]

In Part 1 of this series, we walk through a continuous model improvement machine learning (ML) workflow with Amazon Rekognition Custom Labels and Amazon Augmented AI (Amazon A2I). We explained how we use AWS Step Functions to orchestrate model training and deployment, and custom label detection backed by a human labeling private workforce. We described […]

If you need to integrate image analysis into your business process to detect objects or scenes unique to your business domain, you need to build your own custom machine learning (ML) model. Building a custom model requires advanced ML expertise and can be a technical challenge if you have limited ML knowledge. Because model performance […]

This post was last reviewed and updated June, 2022. When this blog post was initially published, English was the only language supported for the textual field in the items dataset. Now Amazon Personalize supports textual field values in Chinese (simplified and traditional), French, German, Japanese, Portuguese, and Spanish; removes HTML and XML markup and whitespace […]