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Favorite Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). Studio provides a single web-based visual interface where you can perform all ML development steps required to prepare data, as well as build, train, and deploy models. You can quickly upload data, create new
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Shared by AWS Machine Learning June 16, 2021
Favorite Amazon Lookout for Metrics uses machine learning (ML) to automatically detect and diagnose anomalies (outliers from the norm) without requiring any prior ML experience. Amazon CloudWatch provides you with actionable insights to monitor your applications, respond to system-wide performance changes, optimize resource utilization, and get a unified view of
Read More Shared by AWS Machine Learning June 15, 2021
Favorite 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
Read More Shared by AWS Machine Learning June 14, 2021
Favorite 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 this post,
Read More Shared by AWS Machine Learning June 10, 2021
Favorite 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 XGBoost directly.
Read More Shared by AWS Machine Learning June 10, 2021
Favorite 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
Read More Shared by AWS Machine Learning June 10, 2021
Favorite 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 step required to build, train, and deploy models. Studio notebooks
Read More Shared by AWS Machine Learning June 9, 2021
Favorite 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
Read More Shared by AWS Machine Learning June 9, 2021
Favorite 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 performance. For example, let’s say your website uses a regression model
Read More Shared by AWS Machine Learning June 8, 2021
Favorite 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.
Read More Shared by AWS Machine Learning June 8, 2021