AWS Machine Learning Blog

Category: Artificial Intelligence

Simplify continuous learning of Amazon Comprehend custom models using Comprehend flywheel

Amazon Comprehend is a managed AI service that uses natural language processing (NLP) with ready-made intelligence to extract insights about the content of documents. It develops insights by recognizing the entities, key phrases, language, sentiments, and other common elements in a document. The ability to train custom models through the Custom classification and Custom entity […]

Introducing the Amazon Comprehend flywheel for MLOps

The world we live in is rapidly changing, and so are the data and features that companies and customers use to train their models. Retraining models to keep them in sync with these changes is critical to maintain accuracy. Therefore, you need an agile and dynamic approach to keep models up to date and adapt […]

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 […]

Build a GNN-based real-time fraud detection solution using the Deep Graph Library without using external graph storage

Fraud detection is an important problem that has applications in financial services, social media, ecommerce, gaming, and other industries. This post presents an implementation of a fraud detection solution using the Relational Graph Convolutional Network (RGCN) model to predict the probability that a transaction is fraudulent through both the transductive and inductive inference modes. You can deploy our implementation to an Amazon SageMaker endpoint as a real-time fraud detection solution, without requiring external graph storage or orchestration, thereby significantly reducing the deployment cost of the model.

Tune ML models for additional objectives like fairness with SageMaker Automatic Model Tuning

Model tuning is the experimental process of finding the optimal parameters and configurations for a machine learning (ML) model that result in the best possible desired outcome with a validation dataset. Single objective optimization with a performance metric is the most common approach for tuning ML models. However, in addition to predictive performance, there may […]

Achieve high performance at scale for model serving using Amazon SageMaker multi-model endpoints with GPU

Amazon SageMaker multi-model endpoints (MMEs) provide a scalable and cost-effective way to deploy a large number of machine learning (ML) models. It gives you the ability to deploy multiple ML models in a single serving container behind a single endpoint. From there, SageMaker manages loading and unloading the models and scaling resources on your behalf […]

Modular functions design for Advanced Driver Assistance Systems (ADAS) on AWS

Over the last 10 years, a number of players have developed autonomous vehicle (AV) systems using deep neural networks (DNNs). These systems have evolved from simple rule-based systems to Advanced Driver Assistance Systems (ADAS) and fully autonomous vehicles. These systems require petabytes of data and thousands of compute units (vCPUs and GPUs) to train. This […]

Boomi uses BYOC on Amazon SageMaker Studio to scale custom Markov chain implementation

This post is co-written with Swagata Ashwani, Senior Data Scientist at Boomi. Boomi is an enterprise-level software as a service (SaaS) independent software vendor (ISV) that creates developer enablement tooling for software engineers. These tools integrate via API into Boomi’s core service offering. In this post, we discuss how Boomi used the bring-your-own-container (BYOC) approach […]

MLOps deployment best practices for real-time inference model serving endpoints with Amazon SageMaker

After you build, train, and evaluate your machine learning (ML) model to ensure it’s solving the intended business problem proposed, you want to deploy that model to enable decision-making in business operations. Models that support business-critical functions are deployed to a production environment where a model release strategy is put in place. Given the nature […]