Category: Amazon Machine Learning

The ability to predict that a particular customer is at a high risk of churning, while there is still time to do something about it, represents a huge potential revenue source for every online business. Depending on the industry and business objective, the problem statement can be multi-layered. The following are some business objectives based […]

From application forms, to identity documents, recent utility bills, and bank statements, many business processes today still rely on exchanging and analyzing human-readable documents—particularly in industries like financial services and law. In this post, we show how you can use Amazon SageMaker, an end-to-end platform for machine learning (ML), to automate especially challenging document analysis […]

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 […]