AWS Machine Learning Blog

Several recent surveys show that more than 80% of consumers prefer spending with a credit card over cash. Thanks to advances in AI and machine learning (ML), credit card fraud can be detected quickly, which makes credit cards one of the safest and easiest payment methods to use. The challenge with cards, however, is that […]

Healthcare data can be challenging to work with and AWS customers have been looking for solutions to solve certain business challenges with the help of data and machine learning (ML) techniques. Some of the data is structured, such as birthday, gender, and marital status, but most of the data is unstructured, such as diagnosis codes […]

July 2024: This post was reviewed and updated for accuracy. Amazon Redshift ML simplifies the use of machine learning (ML) by using simple SQL statements to create and train ML models from data in Amazon Redshift. You can use Amazon Redshift ML to solve binary classification, multi-class classification, and regression problems and can use either AutoML or […]

July 2023: This post was reviewed for accuracy. To help you fast track your company’s adoption of machine learning (ML), AWS offers educational solutions for developers to get hands-on experience. We like to think of these programs as a fun way for developers to build their skills using ML technologies in real world scenarios. In […]

Data generates new value to businesses through insights and building predictive models. However, although data is plentiful, available data scientists are far and few. Despite our attempts in recent years to produce data scientists from academia and elsewhere, we still see a huge shortage that will continue into the near future. To accelerate model building, […]

July 2023: This post was reviewed for accuracy. The Github repository is maintained up to date. Amazon SageMaker Studio provides a unified, web-based visual interface where you can perform all machine learning (ML) development steps, making data science teams up to 10 times more productive. Studio gives you complete access, control, and visibility into each […]

The last year has made delivering high-quality customer contact center support extremely challenging. Consumers have increasingly abandoned brick-and-mortar retail shopping and traditional banking in favor of digitally enabled experiences, which brings unprecedented call volumes to contact centers. In many cases, call center staff are also working remotely, which makes it even more difficult to meet […]

Data preparation remains a major challenge in the machine learning (ML) space. Data scientists and engineers need to write queries and code to get data from source data stores, and then write the queries to transform this data, to create features to be used in model development and training. All of this data pipeline development […]

October 2021: This post has been updated with a new sample notebook for Amazon SageMaker Studio users.  When a model gets deployed to a production environment, inference speed matters. Models with fast inference speeds require less resources to run, which translates to cost savings, and applications that consume the models’ predictions benefit from the improved […]

Domain experts are increasingly using machine learning (ML) to make faster decisions that lead to better customer outcomes across industries including healthcare, financial services, and many more. ML can provide higher accuracy at lower cost, whereas expert oversight can ensure validation and continuous improvement of sensitive applications like disease diagnosis, credit risk management, and fraud […]