AWS Machine Learning Blog

DXC Technology automates triage of support tickets using AWS machine learning

DXC Technology is a global IT service leader providing end-to-end services on Digital Transformation to businesses and governments. They also provide service management to their clients on-premises and in the cloud.  The incident tickets raised as part of the process need to be resolved quickly to meet their service level agreements (SLA).  DXC has  goals […]

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Deploy trained Keras or TensorFlow models using Amazon SageMaker

Amazon SageMaker makes it easier for any developer or data scientist to build, train, and deploy machine learning (ML) models. While it’s designed to alleviate the undifferentiated heavy lifting from the full life cycle of ML models, Amazon SageMaker’s capabilities can also be used independently of one another; that is, models trained in Amazon SageMaker […]

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Thoughts on Recent Research Paper and Associated Article on Amazon Rekognition

A research paper and associated article published yesterday made claims about the accuracy of Amazon Rekognition. We welcome feedback, and indeed get feedback from folks all the time, but this research paper and article are misleading and draw false conclusions. This blog post shares details which we hope will help clarify several ‎misperceptions and inaccuracies. […]

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Deploy TensorFlow models with Amazon Elastic Inference using a flexible new Python API available in EI-enabled TensorFlow 1.12

Amazon Elastic Inference (EI) now supports the latest version of TensorFlow­–1.12. It provides EIPredictor, a new easy-to-use Python API function for deploying TensorFlow models using EI accelerators. You can now use this new Python API function within your inference scripts as an alternative to using TensorFlow Serving when running TensorFlow models with EI. EIPredictor allows […]

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AWS launches open source Neo-AI project  to accelerate ML deployments on edge devices

 At re:Invent 2018, we announced Amazon SageMaker Neo, a new machine learning feature that you can use to train a machine learning model once and then run it anywhere in the cloud and at the edge. Today, we are releasing the code as the open source Neo-AI project under the Apache Software License. This release […]

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Identifying and working with sensitive healthcare data with Amazon Comprehend Medical

At AWS, I regularly speak with AWS customers and AWS Partner Network (APN) partners about how they are using technology to transform human health. These companies often generate large amounts of health data that they use in a variety of applications, such as population health management and electronic health records. Developers need to find ways to use […]

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Extract and visualize clinical entities using Amazon Comprehend Medical

Amazon Comprehend Medical is a new HIPAA-eligible service that uses machine learning (ML) to extract medical information with high accuracy. This reduces the cost, time, and effort of processing large amounts of unstructured medical text. You can extract entities and relationships like medication, diagnosis, and dosage, and you can also extract protected health information (PHI). […]

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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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