AWS Machine Learning Blog

Category: Artificial Intelligence

Train graph neural nets for millions of proteins on Amazon SageMaker and Amazon DocumentDB (with MongoDB compatibility)

There are over 180,000 unique proteins with 3D structures determined, with tens of thousands new structures resolved every year. This is only a small fraction of the 200 million known proteins with distinctive sequences. Recent deep learning algorithms such as AlphaFold can accurately predict 3D structures of proteins using their sequences, which help scale the […]

Identity verification using Amazon Rekognition

In-person user identity verification is slow to scale, costly, and high friction for users. Machine learning (ML) powered facial recognition technology can enable online user identity verification. Amazon Rekognition offers pre-trained facial recognition capabilities that you can quickly add to your user onboarding and authentication workflows to verify opted-in users’ identities online. No ML expertise […]

Hybrid ML

Introducing hybrid machine learning

Gartner predicts that by the end of 2024, 75% of enterprises will shift from piloting to operationalizing artificial intelligence (AI), and the vast majority of workloads will end up in the cloud in the long run. For some enterprises that plan to migrate to the cloud, the complexity, magnitude, and length of migrations may be […]

Use deep learning frameworks natively in Amazon SageMaker Processing

Until recently, customers who wanted to use a deep learning (DL) framework with Amazon SageMaker Processing faced increased complexity compared to those using scikit-learn or Apache Spark. This post shows you how SageMaker Processing has simplified running machine learning (ML) preprocessing and postprocessing tasks with popular frameworks such as PyTorch, TensorFlow, Hugging Face, MXNet, and […]

Live call analytics and agent assist for your contact center with Amazon language AI services

Update November 2022 (v0.6.0) – Now supports real-time Detected Issues, real-time Call Categories, real-time Supervisor Alerts and integration with the companion Post Call Analytics (PCA) solution, using the new Amazon Transcribe Real-time Call Analytics service. This update also introduces support for Amazon Transcribe Custom Language Models to improve accuracy for domain specific speech, new rich […]

Post call analytics for your contact center with Amazon language AI services

Update November 2022 (v0.4.0) – Now optionally integrates with the companion Live Call Analytics and Agent Assist (LCA) solution to offer both real-time and post-call analytics based on the new Amazon Transcribe Real-time Call Analytics service. See New features. Your contact center connects your business to your community, enabling customers to order products, callers to […]

Build custom Amazon SageMaker PyTorch models for real-time handwriting text recognition

In many industries, including financial services, banking, healthcare, legal, and real estate, automating document handling is an essential part of the business and customer service. In addition, strict compliance regulations make it necessary for businesses to handle sensitive documents, especially customer data, properly. Documents can come in a variety of formats, including digital forms or […]

Achieve 35% faster training with Hugging Face Deep Learning Containers on Amazon SageMaker

Natural language processing (NLP) has been a hot topic in the AI field for some time. As current NLP models get larger and larger, data scientists and developers struggle to set up the infrastructure for such growth of model size. For faster training time, distributed training across multiple machines is a natural choice for developers. […]

Build a computer vision model using Amazon Rekognition Custom Labels and compare the results with a custom trained TensorFlow model

Building accurate computer vision models to detect objects in images requires deep knowledge of each step in the process—from labeling, processing, and preparing the training and validation data, to making the right model choice and tuning the model’s hyperparameters adequately to achieve the maximum accuracy. Fortunately, these complex steps are simplified by Amazon Rekognition Custom […]

Build GAN with PyTorch and Amazon SageMaker

GAN is a generative ML model that is widely used in advertising, games, entertainment, media, pharmaceuticals, and other industries. You can use it to create fictional characters and scenes, simulate facial aging, change image styles, produce chemical formulas synthetic data, and more. For example, the following images show the effect of picture-to-picture conversion. The following […]