AWS Machine Learning

This post is co-authored by Lezgin Bakircioglu, Innovation and Security Manager at Daniel Wellington. In their own words, “Daniel Wellington (DW) is a Swedish fashion brand founded in 2011. Since its inception, i […]

Do you ever wonder what goes behind bringing various levels of autonomy to vehicles? What the vehicle sees (perception) and how the vehicle predicts the actions of different agents in the scene (behavior […]

This post is co-written with Raphey Holmes, Software Engineering Manager, and Jason Mackay, Principal Software Development Engineer, at GoDaddy.
GoDaddy is the world’s largest services platform for e […]

We are living in a digital age. Information that used to be spread by printouts is disseminated at unforeseen speeds through digital formats. In parallel to the inventions of new types of media, an increasing […]

Amazon Redshift ML allows data analysts, developers, and data scientists to train machine learning (ML) models using SQL. In previous posts, we demonstrated how customers can use the automatic model training […]

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 […]

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 […]

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 […]

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 t […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

With the rapid growth of data, many organizations are finding it difficult to analyze their large datasets to gain insights. As businesses rely more and more on automation algorithms, machine learning (ML) has […]

Inventorying store items is a general demand for retail stores and supermarkets. This is usually performed manually by counting items and visually checking the correct placement. Tracking changes in inventory […]

Regulatory mandates, audit requirements, and security policies often call for data visibility and granular data control while using Amazon Simple Storage Service (Amazon S3) for shared datasets. Because data on […]