Category: Amazon Machine Learning

Training your machine learning (ML) model and serving predictions is usually not the end of the ML project. The accuracy of ML models can deteriorate over time, a phenomenon known as model drift. Many factors can cause model drift, such as changes in model features. The accuracy of ML models can also be affected by […]

Today, we’re excited to announce RStudio on Amazon SageMaker, the industry’s first fully-managed RStudio integrated development environment (IDE) in the cloud. You can now bring the current RStudio licenses and migrate your self-managed RStudio environments to Amazon SageMaker in a few simple steps. RStudio is one of the most popular IDEs among R developers for […]

This blog post was co-authored, and includes an introduction, by Aaron Brunko, Senior Vice President, Claims Product at Xactware. Property insurance claims involving the valuation and replacement of personal belongings can be a painful process for everyone involved after a loss. From catastrophic events such as hurricanes, tornados, and wildfires, to theft and vandalism, claim […]

Out-of-stock (OOS) is an essential metric tracked across the retail industry. Brick-and-mortar retailers seek to reduce their costs associated with OOS items, while simultaneously increasing shopper satisfaction without inventory surplus. A product can be OOS in three main ways: Distribution OOS, Store OOS, and Shelf OOS. This post focuses on Shelf OOS. Shelf OOS occurs […]

Implementing an effective fraud prevention system is one of the top priorities for businesses that operate online web or mobile platforms. Businesses report millions of dollars of lost revenue each year due to fraud. Platform abuse and fraud prevention largely remain reactive, and is achieved by studying the profile behavior and transaction history of a […]

This is a guest post written by Gergely Kadi, Senior Systems Engineer and Martin Petraschek-Stummer, Senior Data Engineer at TourRadar. TourRadar is a travel marketplace to connect people to life-enriching travel experiences. When it was launched, TourRadar only offered tours and content in English. As the company grew, we saw an opportunity to expand our […]

One of the main challenges in a machine learning (ML) project implementation is the variety and high number of development artifacts and tools used. This includes code in notebooks, modules for data processing and transformation, environment configuration, inference pipeline, and orchestration code. In production workloads, the ML model created within your development framework is almost […]

Amazon SageMaker is a fully managed service that provides every machine learning (ML) developer and data scientist the ability to build, train, and deploy ML models at scale. Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for ML. Amazon SageMaker Studio provides all the tools you need to take your models from experimentation […]

Feature engineering is expensive and time-consuming, which may lead you to adopt a feature store for managing features across teams and models. Unfortunately, machine learning (ML) lineage solutions have yet to adapt to this new concept of feature management. To achieve the full benefits of a feature store by enabling feature reuse, you need to […]

Defect detection in manufacturing can benefit from machine learning (ML) and computer vision (CV) to reduce operational costs, improve time to market, and increase productivity, quality, and safety. According to McKinsey, the “benefits of defect detection and other Industry 4.0 applications are estimated to create a potential value of $3.7 trillion in 2025 for manufacturers […]