Artificial Intelligence

Neelam Koshiya

Author: Neelam Koshiya

Responsible AI for the payments industry – Part 1

This post explores the unique challenges facing the payments industry in scaling AI adoption, the regulatory considerations that shape implementation decisions, and practical approaches to applying responsible AI principles. In Part 2, we provide practical implementation strategies to operationalize responsible AI within your payment systems.

Host ML models on Amazon SageMaker using Triton: CV model with PyTorch backend

PyTorch is a machine learning (ML) framework based on the Torch library, used for applications such as computer vision and natural language processing. One of the primary reasons that customers are choosing a PyTorch framework is its simplicity and the fact that it’s designed and assembled to work with Python. PyTorch supports dynamic computational graphs, […]

Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

Enterprise customers have multiple lines of businesses (LOBs) and groups and teams within them. These customers need to balance governance, security, and compliance against the need for machine learning (ML) teams to quickly access their data science environments in a secure manner. These enterprise customers that are starting to adopt AWS, expanding their footprint on […]

Incrementally update a dataset with a bulk import mechanism in Amazon Personalize

We are excited to announce that Amazon Personalize now supports incremental bulk dataset imports; a new option for updating your data and improving the quality of your recommendations. Keeping your datasets current is an important part of maintaining the relevance of your recommendations. Prior to this new feature launch, Amazon Personalize offered two mechanisms for […]

Making accurate energy consumption predictions with Amazon Forecast

Amazon Forecast is a fully managed service that uses machine learning (ML) to generate highly accurate forecasts, without requiring any prior ML experience. Forecast is applicable in a wide variety of use cases, including energy demand forecasting, estimating product demand, workforce planning, and computing cloud infrastructure usage. With Forecast, there are no servers to provision […]