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

Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. Today, generative AI can enable people without SQL...

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Best Ways to Eliminate Data Silos

Organizations are acutely aware of the value of data in today's data-driven financial services world. However, many organizations continue to face a significant challenge: data silos. These...

Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker | Amazon Web Services

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative...

Can Sharing Data-Driven Insights Enhance Ad Performance?

The driving element behind decision-making is data. Marketers and advertisers rely on a plethora of data to fine-tune their plans, optimize campaigns, and efficiently reach their target...

Amazon SageMaker Domain in VPC only mode to support SageMaker Studio with auto shutdown Lifecycle Configuration and SageMaker Canvas with Terraform | Amazon Web...

Amazon SageMaker Domain supports SageMaker machine learning (ML) environments, including SageMaker Studio and SageMaker Canvas. SageMaker Studio is a fully integrated development environment (IDE)...

Apply fine-grained data access controls with AWS Lake Formation in Amazon SageMaker Data Wrangler | Amazon Web Services

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. You can...

Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift | Amazon Web Services

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every...

Auto-labeling module for deep learning-based Advanced Driver Assistance Systems on AWS | Amazon Web Services

In computer vision (CV), adding tags to identify objects of interest or bounding boxes to locate the objects is called labeling. It’s one of...

How Light & Wonder built a predictive maintenance solution for gaming machines on AWS | Amazon Web Services

This post is co-written with Aruna Abeyakoon and Denisse Colin from Light and Wonder (L&W). Headquartered in Las Vegas, Light & Wonder, Inc. is...

Reinventing the data experience: Use generative AI and modern data architecture to unlock insights | Amazon Web Services

Implementing a modern data architecture provides a scalable method to integrate data from disparate sources. By organizing data by business domains instead of infrastructure,...

Use Amazon SageMaker Canvas to build machine learning models using Parquet data from Amazon Athena and AWS Lake Formation | Amazon Web Services

Data is the foundation for machine learning (ML) algorithms. One of the most common formats for storing large amounts of data is Apache Parquet...

Get insights on your user’s search behavior from Amazon Kendra using an ML-powered serverless stack | Amazon Web Services

Amazon Kendra is a highly accurate and intelligent search service that enables users to search unstructured and structured data using natural language processing (NLP)...

Build an image search engine with Amazon Kendra and Amazon Rekognition

In this post, we discuss a machine learning (ML) solution for complex image searches using Amazon Kendra and Amazon Rekognition. Specifically, we use the...

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