AWS Machine Learning Blog

Amazon Transcribe now supports real-time transcriptions

Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capability to applications. We’re excited to announce a new feature called Streaming Transcription, which enables users to pass a live audio stream to our service and receive text transcripts in real time. Real-time transcriptions benefit use cases […]

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Easily monitor and visualize metrics while training models on Amazon SageMaker

Data scientists and developers can now quickly and easily access, monitor, and visualize metrics that are computed while training machine learning models on Amazon SageMaker. You can now specify the metrics you want to track by using the AWS Management Console for Amazon SageMaker or by using the Amazon SageMaker Python SDK APIs. After the […]

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Detect suspicious IP addresses with the Amazon SageMaker IP Insights algorithm

Today, we are announcing the new IP Insights algorithm for Amazon SageMaker. IP Insights is an unsupervised learning algorithm for detecting anomalous behavior and usage patterns of IP addresses. In this blog post, we introduce the problem of identifying fraudulent behavior using IP addresses, describe the Amazon SageMaker IP Insights algorithm, demonstrate how you can use it in […]

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Analyze live video at scale in real time using Amazon Kinesis Video Streams and Amazon SageMaker

We are excited to announce the launch of the Amazon Kinesis Video Streams Inference Template (KIT) for Amazon SageMaker. This capability enables customers to attach Kinesis Video streams to Amazon SageMaker endpoints in minutes. This drives real-time inferences without having to use any other libraries or write custom software to integrate the services. The KIT comprises […]

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Amazon SageMaker Automatic Model Tuning becomes more efficient with warm start of hyperparameter tuning jobs

Earlier this year, we launched Amazon SageMaker Automatic Model Tuning, which allows developers and data scientists to save significant time and effort in training and tuning their machine learning models. Today, we are launching warm start of hyperparameter tuning jobs in Automatic Model Tuning. Data scientists and developers can now create a new hyperparameter tuning […]

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Build Your Own Natural Language Models on AWS (no ML experience required)

At AWS re:Invent last year we announced Amazon Comprehend, a natural language processing service which extracts key phrases, places, peoples’ names, brands, events, and sentiment from unstructured text. Comprehend – which is powered by sophisticated deep learning models trained by AWS – allows any developer to add natural language processing to their applications without requiring […]

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Getting Started with Amazon Comprehend custom entities

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. We released an update to Amazon Comprehend enabling support for private, custom entity types. Customers can now train state-of-the-art entity recognition models to extract their specific terms, completely automatically. No machine learning experience required. For example, financial […]

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Amazon Polly adds Italian and Castilian Spanish voices, and Mexican Spanish language support

Amazon Polly is an AWS service that turns text into lifelike speech. This pre-trained service requires no machine learning skills to easily integrate AI into your applications. In addition to the previously available Italian voices Carla and Giorgio, we have now added a second female Italian voice. Listen to the introduction by Bianca. Listen now Voiced […]

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Introduction to Amazon SageMaker Object2Vec 

In this blog post, we’re introducing the Amazon SageMaker Object2Vec algorithm, a new highly customizable multi-purpose algorithm that can learn low dimensional dense embeddings of high dimensional objects. Embeddings are an important feature engineering technique in machine learning (ML). They convert high dimensional vectors into low-dimensional space to make it easier to do machine learning […]

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K-means clustering with Amazon SageMaker

Amazon SageMaker provides several built-in machine learning (ML) algorithms that you can use for a variety of problem types. These algorithms provide high-performance, scalable machine learning and are optimized for speed, scale, and accuracy. Using these algorithms you can train on petabyte-scale data. They are designed to provide up to 10x the performance of the other […]

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