Category: FAANG

Documents are indispensable parts of our professional and personal lives. They give us crucial insights that help us become more efficient, that organize and optimize information, and that even help us to stay competitive. But as documents become increasingly complex, and as the variety of document types continues to expand, it has become increasingly challenging …

Read more “Document AI adds three new capabilities to its OCR engine”

Posted by Pritish Kamath and Pasin Manurangsi, Research Scientists, Google Research Differential privacy (DP) is an approach that enables data analytics and machine learning (ML) with a mathematical guarantee on the privacy of user data. DP quantifies the “privacy cost” of an algorithm, i.e., the level of guarantee that the algorithm’s output distribution for a …

Read more “Differential Privacy Accounting by Connecting the Dots”

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 …

Read more “Power recommendations and search using an IMDb knowledge graph – Part 2”

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 …

Read more “Power recommendation and search using an IMDb knowledge graph – Part 1”

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 …

Read more “Accelerate the investment process with AWS Low Code-No Code services”

In today’s enterprise, the role of the data scientist can seem deceptively simple: generate insights from data and deliver them to decision makers. This process can look like a one-way trip — models are delivered, the business takes action, and the data scientists are left wondering if and how their models have driven impact. Moreover, the sober …

Read more “How Palantir Foundry’s Ontology Deploys Data Science to the Front Line”

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 …

Read more “Automatically retrain neural networks with Renate”

Deploying high-quality, trained machine learning (ML) models to perform either batch or real-time inference is a critical piece of bringing value to customers. However, the ML experimentation process can be tedious—there are a lot of approaches requiring a significant amount of time to implement. That’s why pre-trained ML models like the ones provided in the PyTorch …

Read more “Create Amazon SageMaker models using the PyTorch Model Zoo”