AWS Machine Learning Blog

Tag: Amazon Sagemaker

Modernizing wound care with Spectral MD, powered by Amazon SageMaker

Spectral MD, Inc. is a clinical research stage medical device company that describes itself as “breaking the barriers of light to see deep inside the body.” Recently designated by the FDA as a “Breakthrough Device,” Spectral MD provides an impressive solution to wound care using cutting edge multispectral imaging and deep learning technologies. This Dallas-based […]

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Kinect Energy uses Amazon SageMaker to Forecast energy prices with Machine Learning

The Amazon ML Solutions Lab worked with Kinect Energy recently to build a pipeline to predict future energy prices based on machine learning (ML). We created an automated data ingestion and inference pipeline using Amazon SageMaker and AWS Step Functions to automate and schedule energy price prediction. The process makes special use of the Amazon […]

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Harvesting success using Amazon SageMaker to power Bayer’s digital farming unit

By the year 2050, our planet will need to feed ten billion people. We can’t expand the earth to create more agricultural land, so the solution to growing more food is to make agriculture more productive and less resource-dependent. In other words, there is no room for crop losses or resource waste. Bayer is using […]

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Git integration now available for the Amazon SageMaker Python SDK

Git integration is now available in the Amazon SageMaker Python SDK. You no longer have to download scripts from a Git repository for training jobs and hosting models. With this new feature, you can use training scripts stored in Git repos directly when training a model in the Python SDK. You can also use hosting […]

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Financially empowering Generation Z with behavioral economics, banking, and AWS machine learning

This is a guest blog post by Dante Monaldo, co-founder and CTO of Pluto Money Pluto Money, a San Francisco-based startup, is a free money management app that combines banking, behavioral economics, and machine learning (ML) to guide Generation Z towards their financial goals in college and beyond. We’re building the first mobile bank designed […]

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Machine learning for all developers with edX and Amazon SageMaker

Customers often ask us how to get started when they do not have a deep data science and machine learning (ML) background. At AWS, our goal is to put ML in the hands of every developer and data scientist. AWS Training and Certification has partnered with edX to help you get started quickly and easily with ML with […]

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Using TensorFlow eager execution with Amazon SageMaker script mode

In this blog post, I’ll discuss how to use Amazon SageMaker script mode to train models with TensorFlow’s eager execution mode. Eager execution is the future of TensorFlow; although it is available now as an option in recent versions of TensorFlow 1.x, it will become the default mode of TensorFlow 2. I’ll provide a brief […]

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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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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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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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