AWS Machine Learning

Critical information can be scattered across multiple data sources in your organization, including sources such as Windows file systems stored on Amazon FSx for Windows File Server. You can now use the Amazon […]

Organizations use messaging platforms like Slack to bring the right people together to securely communicate with each other and collaborate to get work done. A Slack workspace captures invaluable organizational […]

This is a post co-written with Bernard Paques, CTO of Storm Reply, and Karl Herkt, Senior Strategist at Dassault Systèmes 3DExcite.
While computer vision can be crucial to industrial maintenance, manufacturing, […]

Data lakes have become the norm in the industry for storing critical business data. The primary rationale for a data lake is to land all types of data, from raw data to preprocessed and postprocessed data, and may […]

In this post, we present a solution for digitizing transactional documents using Amazon Textract and incorporate a human review using Amazon Augmented AI (A2I). You can find the solution source at our GitHub […]

Amazon SageMaker Autopilot is an automated machine learning (AutoML) solution that performs all the tasks you need to complete an end-to-end machine learning (ML) workflow. It explores and prepares your data, […]

Many software as a service (SaaS) providers across various industries are adding machine learning (ML) and artificial intelligence (AI) capabilities to their SaaS offerings to address use cases like personalized […]

Machine learning (ML) helps organizations increase revenue, drive business growth, and reduce cost by optimizing core business functions across multiple verticals, such as demand forecasting, credit scoring, […]

In this post, we will demonstrate how to securely launch notebook instances in a private subnet of an Amazon Virtual Private Cloud (Amazon VPC), with internet access disabled, and to securely connect to Amazon […]

This post is authored by Satish Jha, Intelligent Automation Manager, Matt Docherty, Data Science Manager, Jayesh Muley, Associate Consultant and Tapan Vora, Rapid Prototyping, from ZS Associates.
At ZS […]

This is a guest post by Oliver Frost, data scientist at ImmoScout24, in partnership with Lukas Müller, AWS Solutions Architect.
In 2010, ImmoScout24 released a price index for residential real estate in Germany: […]

With an Amazon SageMaker Domain, you can onboard users with an AWS Identity and Access Management (IAM) execution role different than the Domain execution role. In such case, the onboarded Domain user can’t c […]

Amazon SageMaker Autopilot automatically builds, trains, and tunes the best machine learning (ML) models based on your data, while allowing you to maintain full control and visibility. We have recently announced […]

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning (ML) to discover insights from text. As a fully managed service, Amazon Comprehend requires no ML expertise and can scale […]

This is a guest post authored by Andrew Masek, Software Engineer at The Barcode Registry and Erik Quisling, CEO of The Barcode Registry.
Product counterfeiting is the single largest criminal enterprise in the […]

Whether you’re allocating resources more efficiently for web traffic, forecasting patient demand for staffing needs, or anticipating sales of a company’s products, forecasting is an essential tool across many bus […]

At the 2021 AWS re:Invent conference in Las Vegas, we demoed Read For Me at the AWS Builders Fair—a website that helps the visually impaired hear documents.

For better quality, view the video here. […]

In football, as in many sports, discussions about individual players have always been part of the fun. “Who is the best scorer?” or “Who is the king of defenders?” are questions perennially debated by fans, a […]

The importance of set pieces in football (or soccer in the US) has been on the rise in recent years: now more than one quarter of all goals are scored via set pieces. Free kicks and corners generally create the […]

Deploying and managing machine learning (ML) models at the edge requires a different set of tools and skillsets as compared to the cloud. This is primarily due to the hardware, software, and networking […]