Category: Amazon Machine Learning

This blog post was last reviewed and updated August, 2022 with a generator-based approach for video payloads of longer duration. AWS customers are increasingly using computer vision (CV) models on large input payloads that can take a few minutes of processing time. For example, space technology companies work with a stream of high-resolution satellite imagery […]

According to a recent study, defective products cost industries over $2 billion from 2012–2017. Defect detection within manufacturing is an important business use case, especially in high-value product industries like the automotive industry. This allows for early diagnosis of anomalies to improve production line efficacy and product quality, and saves capital costs. Although advanced anomaly […]

A continuous integration and continuous delivery (CI/CD) pipeline helps you automate steps in your machine learning (ML) applications such as data ingestion, data preparation, feature engineering, modeling training, and model deployment. A pipeline across multiple AWS accounts improves security, agility, and resilience because an AWS account provides a natural security and access boundary for your […]

You can deploy machine learning (ML) models for real-time inference with large libraries or pre-trained models. Common use cases include sentiment analysis, image classification, and search applications. These ML jobs typically vary in duration and require instant scaling to meet peak demand. You want to process latency-sensitive inference requests and pay only for what you […]

Proactively detecting unusual or unexpected variances in your business metrics and reducing false alarms can help you stay on top of sudden changes and improve your business performance. Accurately identifying the root cause of deviation from normal business metrics and taking immediate steps to remediate an anomaly can not only boost user engagement but also […]

In the era of Big Data, businesses are faced with a deluge of time series data. This data is not just available in high volumes, but is also highly nuanced. Amazon Forecast Deep Learning algorithms such as DeepAR+ and CNN-QR build representations that effectively capture common trends and patterns across these numerous time series. These […]

This is a guest post by Shae Selix (Staff Data Scientist at Calm) and Luis Lopez Soria (Sr. AI/ML Specialist SA at AWS). Today, content is proliferating. It’s being produced in many different forms by a host of content providers, both large and small. Whether it’s on-demand video, music, podcasts, or other forms of rich […]

When we analyze images, we may want to incorporate other metadata related to the image. Examples include when and where the image was taken, who took the image, as well as what is featured in the image. One way to represent this metadata is to use a JSON format, which is well-suited for a document […]

Amazon Rekognition Custom Labels allows you to extend the object and scene detection capabilities of Amazon Rekognition to extract information from images that is uniquely helpful to your business. For example, you can find your logo in social media posts, identify your products on store shelves, classify machine parts in an assembly line, distinguish healthy […]

In this post, we discuss how to use the AWS Cloud and its services to accelerate observational studies for life sciences customers. We provide a reference architecture for architects, business owners, and technology decision-makers in the life sciences industry to automate the processes in clinical studies. Observational studies lead the way in research, allowing you […]