AWS Machine Learning Blog

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 […]

Update August 2024 (v0.9.0) – Now includes audio streaming from softphone or meeting web apps, sample server for Talkdesk audio integration, automatic language identification, Anthropic’s Claude-3 LLM models on Amazon Bedrock, Knowledge Bases on Amazon Bedrock, and much more. For details, see New features. Your contact center connects your business to your community, enabling customers […]

January 2024 (v0.7.5) – This latest release includes support for larger prompts by storing them in DynamoDB instead of SSM Parameter Store. December 2023 (v0.7.4) – This release includes the ability to upload call recordings directly from the UI, and a status indicator field showing call recordings that are being processed. This release also includes […]

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 […]

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. […]

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 […]

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 […]

Customers in many different domains tend to work with multiple sources for their data: object-based storage like Amazon Simple Storage Service (Amazon S3), relational databases like Amazon Relational Database Service (Amazon RDS), or data warehouses like Amazon Redshift. Machine learning (ML) practitioners are often driven to work with objects and files instead of databases and […]

AutoML is a powerful capability, provided by Amazon SageMaker Autopilot, that allows non-experts to create machine learning (ML) models to invoke in their applications. The problem that we want to solve arises when, due to governance constraints, Amazon SageMaker resources can’t be deployed in the same AWS account where they are used. Examples of such […]

Multi-object tracking (MOT) in video analysis is increasingly in demand in many industries, such as live sports, manufacturing, surveillance, and traffic monitoring. For example, in live sports, MOT can track soccer players in real time to analyze physical performance such as real-time speed and moving distance. Previously, most methods were designed to separate MOT into […]