Category: AWS Glue

Searching for insights in a repository of free-form text documents can be like finding a needle in a haystack. A traditional approach might be to use word counting or other basic analysis to parse documents, but with the power of Amazon AI and machine learning (ML) tools, we can gather deeper understanding of the content. […]

In this post, we show how the Carrier and AWS teams applied ML to predict faults across large fleets of equipment using a single model. We first highlight how we use AWS Glue for highly parallel data processing. We then discuss how Amazon SageMaker helps us with feature engineering and building a scalable supervised deep learning model.

This post is co-written with Suhyoung Kim, General Manager at KakaoGames Data Analytics Lab. Kakao Games is a top video game publisher and developer headquartered in South Korea. It specializes in developing and publishing games on PC, mobile, and virtual reality (VR) serving globally. In order to maximize its players’ experience and improve the efficiency […]

Organizations are using machine learning (ML) and AI services to enhance customer experience, reduce operational cost, and unlock new possibilities to improve business outcomes. Data underpins ML and AI use cases and is a strategic asset to an organization. As data is growing at an exponential rate, organizations are looking to set up an integrated, […]

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). It provides a single, web-based visual interface where you can perform all ML development steps, including preparing data and building, training, and deploying models. AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and […]

With personalized content more likely to drive customer engagement, businesses continuously seek to provide tailored content based on their customer’s profile and behavior. Recommendation systems in particular seek to predict the preference an end-user would give to an item. Some common use cases include product recommendations on online retail stores, personalizing newsletters, generating music playlist […]

Organizations are increasingly building and using machine learning (ML)-powered solutions for a variety of use cases and problems, including predictive maintenance of machine parts, product recommendations based on customer preferences, credit profiling, content moderation, fraud detection, and more. In many of these scenarios, the effectiveness and benefits derived from these ML-powered solutions can be further […]

Launched at AWS re:Invent 2020, Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With Pipelines, you can create, automate, and manage end-to-end ML workflows at scale. You can extend your pipelines to include steps for tasks performed outside of Amazon SageMaker by taking advantage […]

Do you want to monitor your business metrics and detect anomalies in your existing streaming data pipelines? Amazon Lookout for Metrics is a service that uses machine learning (ML) to detect anomalies in your time series data. The service goes beyond simple anomaly detection. It allows developers to set up autonomous monitoring for important metrics […]

This post is co-written with Liam Pearson, a Data Scientist at Genworth Mortgage Insurance Australia Limited. Genworth Mortgage Insurance Australia Limited is a leading provider of lenders mortgage insurance (LMI) in Australia; their shares are traded on Australian Stock Exchange as ASX: GMA. Genworth Mortgage Insurance Australia Limited is a lenders mortgage insurer with over […]