Category: Amazon Machine Learning

For building any generative AI application, enriching the large language models (LLMs) with new data is imperative. This is where the Retrieval Augmented Generation (RAG) technique comes in. RAG is a machine learning (ML) architecture that uses external documents (like Wikipedia) to augment its knowledge and achieve state-of-the-art results on knowledge-intensive tasks. For ingesting these […]

In pursuit of a more efficient and customer-centric support system, organizations are deploying cutting-edge generative AI applications. These applications are designed to excel in four critical areas: multi-lingual support, sentiment analysis, personally identifiable information (PII) detection, and conversational search capabilities. Customers worldwide can now engage with the applications in their preferred language, and the applications […]

In this era of big data, organizations worldwide are constantly searching for innovative ways to extract value and insights from their vast datasets. Apache Spark offers the scalability and speed needed to process large amounts of data efficiently. Amazon EMR is the industry-leading cloud big data solution for petabyte-scale data processing, interactive analytics, and machine […]

In this post, we discuss how AWS can help you successfully address the challenges of extracting insights from unstructured data. We discuss various design patterns and architectures for extracting and cataloging valuable insights from unstructured data using AWS. Additionally, we show how to use AWS AI/ML services for analyzing unstructured data.

This post is a guest post co-written with SeonJeong Lee, JaeRyun Yim, and HyeonSeok Yang from Encored Technologies. Encored Technologies (Encored) is an energy IT company in Korea that helps their customers generate higher revenue and reduce operational costs in renewable energy industries by providing various AI-based solutions. Encored develops machine learning (ML) applications predicting […]

Data mesh is a new approach to data management. Companies across industries are using a data mesh to decentralize data management to improve data agility and get value from data. However, when a data producer shares data products on a data mesh self-serve web portal, it’s neither intuitive nor easy for a data consumer to […]

This post is co-written with Byungjun Choi and Sangha Yang from SikSin. SikSin is a technology platform connecting customers with restaurant partners serving their multiple needs. Customers use the SikSin platform to search and discover restaurants, read and write reviews, and view photos. From the restaurateurs’ perspective, SikSin enables restaurant partners to engage and acquire […]

The importance of data warehouses and analytics performed on data warehouse platforms has been increasing steadily over the years, with many businesses coming to rely on these systems as mission-critical for both short-term operational decision-making and long-term strategic planning. Traditionally, data warehouses are refreshed in batch cycles, for example, monthly, weekly, or daily, so that […]

It only takes one groundbreaking invention—one iconic idea that solves a widespread pain point for customers—to create or transform an industry forever. From the invention of the telegraph, to the discovery of GPS, to the earliest cloud computing services, history is filled with examples of these “eureka” moments that continue to have long-lasting impacts on […]

August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. This post is co-written by Kanti Singh, Director of Data & Analytics at Fresenius Medical Care. Fresenius Medical Care is the world’s leading provider of kidney care […]