Category: Advanced (300)

This post is co-authored by Marios Skevofylakas, Jason Ramchandani and Haykaz Aramyan from Refinitiv, An LSEG Business. Financial service providers often need to identify relevant news, analyze it, extract insights, and take actions in real time, like trading specific instruments (such as commodities, shares, funds) based on additional information or context of the news item. […]

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so […]

Machine learning (ML) applications are complex to deploy and often require the ability to hyper-scale, and have ultra-low latency requirements and stringent cost budgets. Use cases such as fraud detection, product recommendations, and traffic prediction are examples where milliseconds matter and are critical for business success. Strict service level agreements (SLAs) need to be met, […]

The last decade of the Industry 4.0 revolution has shown the value and importance of machine learning (ML) across verticals and environments, with more impact on manufacturing than possibly any other application. Organizations implementing a more automated, reliable, and cost-effective Operational Technology (OT) strategy have led the way, recognizing the benefits of ML in predicting […]

Today, companies are establishing feature stores to provide a central repository to scale ML development across business units and data science teams. As feature data grows in size and complexity, data scientists need to be able to efficiently query these feature stores to extract datasets for experimentation, model training, and batch scoring. Amazon SageMaker Feature […]

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 […]

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 […]

Today we announce the general availability of Renate, an open-source Python library for automatic model retraining. The library provides continual learning algorithms able to incrementally train a neural network as more data becomes available. By open-sourcing Renate, we would like to create a venue where practitioners working on real-world machine learning systems and researchers interested […]

In an increasingly data-centric world, enterprises must focus on gathering both valuable physical information and generating the information that they need but can’t easily capture. Data access, regulation, and compliance are an increasing source of friction for innovation in analytics and artificial intelligence (AI). For highly regulated sectors such as Financial Services, Healthcare, Life Sciences, […]

Manually inspecting data quality and cleaning data is a painful and time-consuming process that can take a huge chunk of a data scientist’s time on a project. According to a 2020 survey of data scientists conducted by Anaconda, data scientists spend approximately 66% of their time on data preparation and analysis tasks, including loading (19%), cleaning (26%), […]