Category: Artificial Intelligence

Amazon Polly is a service that turns text into natural-sounding speech, using dozens of voices in more than 30 languages. You can use it for all sorts of applications, ranging from talking animated avatars, to lifelike virtual agents that answer customer support requests, to automated newscasters reading stories aloud. You can have Amazon Polly return […]

SageMaker Projects give organizations the ability to easily setup and standardize developer environments for data scientists and CI/CD systems for MLOps Engineers. With SageMaker Projects, MLOps engineers or organization admins can define templates which bootstrap the ML Workflow with source version control, automated ML Pipelines, and a set of code to quickly start iterating over […]

Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities, such as the creation of fake accounts or online payment fraud. Amazon Fraud Detector uses machine learning (ML) under the hood and is based on over 20 years of fraud detection expertise from Amazon. It automatically identifies […]

Feature engineering is expensive and time-consuming, which may lead you to adopt a feature store for managing features across teams and models. Unfortunately, machine learning (ML) lineage solutions have yet to adapt to this new concept of feature management. To achieve the full benefits of a feature store by enabling feature reuse, you need to […]

Defect detection in manufacturing can benefit from machine learning (ML) and computer vision (CV) to reduce operational costs, improve time to market, and increase productivity, quality, and safety. According to McKinsey, the “benefits of defect detection and other Industry 4.0 applications are estimated to create a potential value of $3.7 trillion in 2025 for manufacturers […]

In this post, you will learn how to create a multi-branch training MLOps continuous integration and continuous delivery (CI/CD) pipeline using AWS CodePipeline and AWS CodeCommit, in addition to Jenkins and GitHub. I discuss the concept of experiment branches, where data scientists can work in parallel and eventually merge their experiment back into the main […]

Amazon SageMaker JumpStart helps you quickly and easily get started with machine learning (ML) and provides a set of solutions for the most common use cases that can be trained and deployed readily with just a few clicks. JumpStart also includes a collection of multimodal financial text analysis tools, including example notebooks, text models, and […]

AWS customers are increasingly training and fine-tuning large computer vision (CV) models with hundreds of terabytes of data and millions of parameters. For example, advanced driver assistance systems (ADAS) train perception models to detect pedestrians, road signs, vehicles, traffic lights, and other objects. Identity verification systems for the financial services industry train CV models to […]

AWS customers are increasingly building applications that are enhanced with predictions from computer vision models. For example, a fitness application monitors the body posture of users while exercising in front of a camera and provides live feedback to the users as well as periodic insights. Similarly, an inventory inspection tool in a large warehouse captures […]

As more European organizations move from experimentation to production for AI projects, the importance of running these projects on a scalable, secure, and cost-efficient platform becomes clear. Building AI solutions from scratch is often beyond the capabilities of many organizations, especially because it requires in-house AI expertise, which is in short supply. According to analyst […]