AWS Machine Learning Blog

Power your website with on-demand translated reviews using Amazon Translate

Amazon Translate is a high-quality neural machine translation service that uses advanced deep learning techniques to provide fast language translation of content from a source language to a target language, chosen among the supported pairs. It enables developers to easily invoke an API providing the text to be translated and obtain its translated version in real-time, hiding the complexity of building a neural machine translation model.

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Easily train models using datasets labeled by Amazon SageMaker Ground Truth

Data scientists and developers can now easily train machine learning models on datasets labeled by Amazon SageMaker Ground Truth. Amazon SageMaker Training now accepts the labeled datasets produced in augmented manifest format as input through both AWS Management Console and Amazon SageMaker Python SDK APIs. Last month during AWS re:Invent, we launched Amazon SageMaker Ground […]

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Analyzing contact center calls—Part 1: Use Amazon Transcribe and Amazon Comprehend to analyze customer sentiment

Contact centers aiming to improve overall operational efficiency have an imperative to understand caller-agent dynamics. In part one of this two-part blog post series we’ll show you how you can use Amazon Transcribe and Amazon Comprehend to transform call recordings from audio to text and then run sentiment analysis on the transcripts. We will demonstrate how […]

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Scalable multi-node training with TensorFlow

We’ve heard from customers that scaling TensorFlow training jobs to multiple nodes and GPUs successfully is hard. TensorFlow has distributed training built-in, but it can be difficult to use. Recently, we made optimizations to TensorFlow and Horovod to help AWS customers scale TensorFlow training jobs to multiple nodes and GPUs. With these improvements, any AWS customer […]

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Amazon SageMaker Automatic Model Tuning now supports early stopping of training jobs

In June 2018, we launched Amazon SageMaker Automatic Model Tuning, a feature that automatically finds well-performing hyperparameters to train a machine learning model with. Unlike model parameters learned during training, hyperparameters are set before the learning process begins. A typical example of the use of hyperparameters is the learning rate of stochastic gradient procedures. Using […]

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Build a serverless Twitter reader using AWS Fargate

In a previous post, Ben Snively and Viral Desai showed us how to build a social media dashboard using serverless technology. The social media dashboard reads tweets with the #AWS hashtag, uses machine learning based services to do translation, and natural language processing (NLP) to determine topics, entities, and sentiment analysis. Finally, it aggregates this […]

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Anomaly detection on Amazon DynamoDB Streams using the Amazon SageMaker Random Cut Forest algorithm

Have you considered introducing anomaly detection technology to your business? Anomaly detection is a technique used to identify rare items, events, or observations which raise suspicion by differing significantly from the majority of the data you are analyzing.  The applications of anomaly detection are wide-ranging including the detection of abnormal purchases or cyber intrusions in […]

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Announcing the Winners of the 2018 AWS AI Hackathon

We’re excited to announce the winners of the 2018 AWS AI Hackathon.  Horacio Canales has won first place with his “Second Alert” project. This project enables users from around the world to identify missing persons, including human trafficking victims, children too young to remember their family members’ names, and mentally handicapped individuals. Horacio built the […]

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Amazon SageMaker now comes with new capabilities for accelerating machine learning experimentation

Data scientists and developers can now quickly and easily organize, track, and evaluate their machine learning (ML) model training experiments on Amazon SageMaker. We are introducing a new Amazon SageMaker Search capability that lets you find and evaluate the most relevant model training runs from the hundreds and thousands of your Amazon SageMaker model training […]

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Amazon SageMaker notebooks now support Git integration for increased persistence, collaboration, and reproducibility

It’s now possible to associate GitHub, AWS CodeCommit, and any self-hosted Git repository with Amazon SageMaker notebook instances to easily and securely collaborate and ensure version-control with Jupyter Notebooks. In this blog post, I’ll elaborate on the benefits of using Git-based version-control systems and how to set up your notebook instances to work with Git repositories. Data […]

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