An overview of deep learning architectures that help computers detect objects, a key technology used in self-driving cars and healthcare.

The object detection required for machine vision applications such as autonomous driving, smart manufacturing, and surveillance applications depends on AI modeling. The goal now is to improve the ...

Safe Pro Surpasses 1,000,000 Drone Images Analyzed Milestone Using Its Novel AI Algorithms & Machine Learning Models on AWS Cloud Advancing Small Object Threat Detection ...

Microsoft improved the object detection capabilities of its ML.NET machine learning framework for .NET developers, adding the ability to train custom models with Model Builder in Visual Studio.

FOMO is a deep learning object detection model that weighs less than 200 kilobytes.

In this article, the author discusses a machine learning pipeline with observability built-in for credit card fraud detection using tools like MLflow, Streamlit, Prometheus, Grafana, and Evidently AI.

Machine learning models can run in the cloud, but that adds latency and requires an internet connection — non-starters for a lot of common use cases.

Bespoke fraud ML models are powered by algorithms that learn from historical data, picking up on behaviors and characteristics commonly associated with fraud.