Category: FAANG

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”

To produce any sufficiently accurate machine learning model, the process requires tuning parameters and hyperparameters. Your model’s parameters are variables that your chosen machine learning technique uses to adjust to your data, like weights in neural networks to minimize loss. Hyperparameters are variables that control the training process itself. For example, in a multilayer perceptron, …

Read more “Perform hyperparameter tuning using R and caret on Vertex AI”