AWS Machine Learning Blog

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

Update October 2023 (v0.8.7) – In this release: Generative transcript summarization and LLM powered Agent Assist are now enabled by default, and now use Amazon Bedrock by default. Generative transcript summarization now generates multiple call insights and can be easily extended and customized. The Agent Assist bot now offers on-demand in-progress call summarization to quickly […]

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

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 […]

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 […]

Process Amazon Redshift data and schedule a training pipeline with Amazon SageMaker Processing and Amazon SageMaker Pipelines

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 […]

Add AutoML functionality with Amazon SageMaker Autopilot across accounts

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 […]

Train and deploy a FairMOT model with Amazon SageMaker

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 […]

Distributed Mask RCNN training with Amazon SageMakerCV

Computer vision algorithms are at the core of many deep learning applications. Self-driving cars, security systems, healthcare, logistics, and image processing all incorporate various aspects of computer vision. But despite their ubiquity, training computer vision algorithms, like Mask or Cascade RCNN, is hard. These models employ complex architectures, train on large datasets, and require computer […]