AWS Machine Learning Blog

Ensure consistency in data processing code between training and inference in Amazon SageMaker

In this blog post, we’ll show you how to deploy an inference pipeline consisting of pre-processing using SparkML, inferences using XGBoost, and post-processing using SparkML. For this particular example, we are using the Car Evaluation Data Set from UCI’s Machine Learning Repository and training an XGBoost model to predict the condition of a car (i.e. unacceptable, acceptable, good, or very good).

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How simpleshow uses Amazon Polly to voice stories in their explainer videos

More than ten years ago, simpleshow started to help their customers explain materials, ideas, and products by using three-minute animated explainer videos. These explainer videos use two hands and simple, black and white illustration to lead viewers through a story. Today, the company also provides mysimpleshow.com, a platform that allows anyone to produce high-quality explainer […]

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Automated and continuous deployment of Amazon SageMaker models with AWS Step Functions

Amazon SageMaker is a complete machine learning (ML) workflow service for developing, training, and deploying models, lowering the cost of building solutions, and increasing the productivity of data science teams. Amazon SageMaker comes with many predefined algorithms. You can also create your own algorithms by supplying Docker images, a training image to train your model […]

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Use AWS Machine Learning to Analyze Customer Calls from Contact Centers (Part 2): Automate, Deploy, and Visualize Analytics using Amazon Transcribe, Amazon Comprehend, AWS CloudFormation, and Amazon QuickSight

In the previous blog post, we showed you how to string together Amazon Transcribe and Amazon Comprehend to be able to conduct sentiment analysis on call conversations from contact centers. Here, we demonstrate how to leverage AWS CloudFormation to automate the process and deploy your solution at scale. Solution Architecture The following diagram illustrates architecture that […]

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Transcribe speech in three new languages: French, Italian, and Brazilian Portuguese

We’re excited to announce that Amazon Transcribe now supports automatic speech recognition in three new languages: French, Italian, and Brazilian Portuguese. These new languages expand upon the 5 languages already available in Amazon Transcribe: US English, US Spanish, Australian English, British English, and Canadian French. Using the Amazon Transcribe API, you can analyze audio files […]

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Amazon SageMaker adds Scikit-Learn support

Amazon SageMaker now comes pre-configured with the Scikit-Learn machine learning library in a Docker container. Scikit-Learn is popular choice for data scientists and developers because it provides efficient tools for data analysis and high quality implementations of popular machine learning algorithms through a consistent Python interface and well documented APIs. Scikit-Learn executes quickly and can […]

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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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