AWS Machine Learning Blog

Tag: AI/ML

Overcome the machine learning cold start challenge in fraud detection using Amazon Fraud Detector

As more businesses increase their online presence to serve their customers better, new fraud patterns are constantly emerging. In today’s ever-evolving digital landscape, where fraudsters are becoming more sophisticated in their tactics, detecting and preventing such fraudulent activities has become paramount for companies and financial institutions. Traditional rule-based fraud detection systems are capped in their […]

Architect personalized generative AI SaaS applications on Amazon SageMaker

The AI landscape is being reshaped by the rise of generative models capable of synthesizing high-quality data, such as text, images, music, and videos. The course toward democratization of AI helped to further popularize generative AI following the open-source releases for such foundation model families as BERT, T5, GPT, CLIP and, most recently, Stable Diffusion. […]

Scaling Large Language Model (LLM) training with Amazon EC2 Trn1 UltraClusters

Modern model pre-training often calls for larger cluster deployment to reduce time and cost. At the server level, such training workloads demand faster compute and increased memory allocation. As models grow to hundreds of billions of parameters, they require a distributed training mechanism that spans multiple nodes (instances). In October 2022, we launched Amazon EC2 […]

Amazon SageMaker JumpStart now offers Amazon Comprehend notebooks for custom classification and custom entity detection

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning (ML) to discover insights from text. Amazon Comprehend provides customized features, custom entity recognition, custom classification, and pre-trained APIs such as key phrase extraction, sentiment analysis, entity recognition, and more so you can easily integrate NLP into your applications. We recently added […]

Create synthetic data for computer vision pipelines on AWS

Collecting and annotating image data is one of the most resource-intensive tasks on any computer vision project. It can take months at a time to fully collect, analyze, and experiment with image streams at the level you need in order to compete in the current marketplace. Even after you’ve successfully collected data, you still have […]

QuickSight Visualization

Get better insight from reviews using Amazon Comprehend

“85% of buyers trust online reviews as much as a personal recommendation” – Gartner Consumers are increasingly engaging with businesses through digital surfaces and multiple touchpoints. Statistics show that the majority of shoppers use reviews to determine what products to buy and which services to use. As per Spiegel Research Centre, the purchase likelihood for […]

Feature Group Update workflow

Simplify iterative machine learning model development by adding features to existing feature groups in Amazon SageMaker Feature Store

Feature engineering is one of the most challenging aspects of the machine learning (ML) lifecycle and a phase where the most amount of time is spent—data scientists and ML engineers spend 60–70% of their time on feature engineering. AWS introduced Amazon SageMaker Feature Store during AWS re:Invent 2020, which is a purpose-built, fully managed, centralized […]

Predict types of machine failures with no-code machine learning using Amazon SageMaker Canvas

Predicting common machine failure types is critical in manufacturing industries. Given a set of characteristics of a product that is tied to a given type of failure, you can develop a model that can predict the failure type when you feed those attributes to a machine learning (ML) model. ML can help with insights, but […]

Demystifying machine learning at the edge through real use cases

Edge is a term that refers to a location, far from the cloud or a big data center, where you have a computer device (edge device) capable of running (edge) applications. Edge computing is the act of running workloads on these edge devices. Machine learning at the edge (ML@Edge) is a concept that brings the […]