Favorite 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,

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Shared by AWS Machine Learning October 23, 2021

Favorite 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

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Shared by AWS Machine Learning October 23, 2021

Favorite 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

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Shared by AWS Machine Learning October 23, 2021

Favorite 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

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Shared by AWS Machine Learning October 21, 2021

Favorite 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

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Shared by AWS Machine Learning October 20, 2021

Favorite 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

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Shared by AWS Machine Learning October 20, 2021

Favorite Labeling is an indispensable stage of data preprocessing in supervised learning. Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning. Ground Truth helps improve the quality of labels through annotation consolidation and audit workflows.

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Shared by AWS Machine Learning October 18, 2021

Favorite Dealing with content in large scale is often challenging, costly, and a heavy lift operation. The volume of user-generated and third-party content has been increasing substantially in industries like social media, ecommerce, online advertising, and media sharing. Customers may want to review this content to ensure that it follows

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Shared by AWS Machine Learning October 18, 2021

Favorite This post is co-written by Ian Avilez and Tim Pavlick from HawkEye 360. HawkEye 360 is a commercial radio frequency (RF) constellation, data, and analytics provider. Their signals of interest include very high frequency (VHF) push-to-talk radios, maritime radar systems, Automatic Identification System (AIS) beacons, emergency beacons, and more.

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Shared by AWS Machine Learning October 15, 2021

Favorite This is a guest post by Imperva, a solutions provider for cybersecurity.  Imperva is a cybersecurity leader, headquartered in California, USA, whose mission is to protect data and all paths to it. In the last few years, we’ve been working on integrating machine learning (ML) into our products. This

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Shared by AWS Machine Learning October 14, 2021