AWS Machine Learning Blog

Category: AWS Glue

The solution architecture and the process flow is shown.

Build a robust text-to-SQL solution generating complex queries, self-correcting, and querying diverse data sources

Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. Today, generative AI can enable people without SQL knowledge. This generative AI task is called text-to-SQL, which generates SQL queries from natural language processing (NLP) and converts text into semantically correct SQL. The solution in this post aims to […]

Overview for ETL pipeline using SageMaker Processing

Streamlining ETL data processing at with Amazon SageMaker

This post outlines the ETL pipeline we developed for feature processing for training and deploying a job recommender model at Our pipeline uses SageMaker Processing jobs for efficient data processing and feature extraction at a large scale. Feature extraction code is implemented in Python enabling the use of popular ML libraries to perform feature extraction at scale, without the need to port the code to use PySpark.

Visualize an Amazon Comprehend analysis with a word cloud in Amazon QuickSight

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. […]

How Carrier predicts HVAC faults using AWS Glue and Amazon SageMaker

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.

How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

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 […]

Large-scale feature engineering with sensitive data protection using AWS Glue interactive sessions and Amazon SageMaker Studio

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, […]

Prepare data at scale in Amazon SageMaker Studio using serverless AWS Glue interactive sessions

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 […]

Create a batch recommendation pipeline using Amazon Personalize with no code

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 […]

Build a predictive maintenance solution with Amazon Kinesis, AWS Glue, and Amazon SageMaker

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 […]

Extend Amazon SageMaker Pipelines to include custom steps using callback steps

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 […]