Artificial Intelligence
Category: Analytics
Extract non-PHI data from Amazon HealthLake, reduce complexity, and increase cost efficiency with Amazon Athena and Amazon SageMaker Canvas
In today’s highly competitive market, performing data analytics using machine learning (ML) models has become a necessity for organizations. It enables them to unlock the value of their data, identify trends, patterns, and predictions, and differentiate themselves from their competitors. For example, in the healthcare industry, ML-driven analytics can be used for diagnostic assistance and […]
Get smarter search results with the Amazon Kendra Intelligent Ranking and OpenSearch plugin
If you’ve had the opportunity to build a search application for unstructured data (i.e., wiki, informational web sites, self-service help pages, internal documentation, etc.) using open source or commercial-off-the-shelf search engines, then you’re probably familiar with the inherent accuracy challenges involved in getting relevant search results. The intended meaning of both query and document can […]
Power recommendations and search using an IMDb knowledge graph – Part 3
This three-part series demonstrates how to use graph neural networks (GNNs) and Amazon Neptune to generate movie recommendations using the IMDb and Box Office Mojo Movies/TV/OTT licensable data package, which provides a wide range of entertainment metadata, including over 1 billion user ratings; credits for more than 11 million cast and crew members; 9 million […]
Connecting Amazon Redshift and RStudio on Amazon SageMaker
Last year, we announced the general availability of RStudio on Amazon SageMaker, the industry’s first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) […]
Power recommendations and search using an IMDb knowledge graph – Part 2
This three-part series demonstrates how to use graph neural networks (GNNs) and Amazon Neptune to generate movie recommendations using the IMDb and Box Office Mojo Movies/TV/OTT licensable data package, which provides a wide range of entertainment metadata, including over 1 billion user ratings; credits for more than 11 million cast and crew members; 9 million […]
Power recommendation and search using an IMDb knowledge graph – Part 1
The IMDb and Box Office Mojo Movies/TV/OTT licensable data package provides a wide range of entertainment metadata, including over 1 billion user ratings; credits for more than 11 million cast and crew members; 9 million movie, TV, and entertainment titles; and global box office reporting data from more than 60 countries. Many AWS media and […]
Accelerate the investment process with AWS Low Code-No Code services
The last few years have seen a tremendous paradigm shift in how institutional asset managers source and integrate multiple data sources into their investment process. With frequent shifts in risk correlations, unexpected sources of volatility, and increasing competition from passive strategies, asset managers are employing a broader set of third-party data sources to gain a […]
Prepare data from Amazon EMR for machine learning using Amazon SageMaker Data Wrangler
Data preparation is a principal component of machine learning (ML) pipelines. In fact, it is estimated that data professionals spend about 80 percent of their time on data preparation. In this intensive competitive market, teams want to analyze data and extract more meaningful insights quickly. Customers are adopting more efficient and visual ways to build […]
Apply fine-grained data access controls with AWS Lake Formation and Amazon EMR from Amazon SageMaker Studio
June 2023: This post was reviewed and updated to reflect the launch of EMR release 6.10 Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) that enables data scientists and developers to perform every step of the ML workflow, from preparing data to building, training, tuning, and deploying models. Studio […]
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, […]