AWS Machine Learning Blog

Category: SageMaker

Speed up training on Amazon SageMaker using Amazon FSx for Lustre and Amazon EFS file systems

Amazon SageMaker provides a fully managed service for data science and machine learning workflows. One of the most important capabilities of Amazon SageMaker is its ability to run fully managed training jobs to train machine learning models. Now, you can speed up your training job runs by training machine learning models from data stored in Amazon FSx […]

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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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Using model attributes to track your training runs on Amazon SageMaker

With a few clicks in the Amazon SageMaker console or a few one-line API calls, you can now quickly search, filter, and sort your machine learning (ML) experiments using key model attributes, such as hyperparameter values and accuracy metrics, to help you more quickly identify the best models for your use case and get to […]

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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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Building, training, and deploying fastai models with Amazon SageMaker

Deep learning is changing the world. However, much of the foundation work, such as building containers, can slow you down. This post describes how you can build, train, and deploy fastai models into Amazon SageMaker training and hosting by using the Amazon SageMaker Python SDK and a PyTorch base image. This helps you avoid the […]

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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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Enabling healthcare access from home: Electronic Caregiver’s AWS-powered virtual caregiver  

When Electronic Caregiver’s founder and CEO, Anthony Dohrmann, started the company a decade ago, he was reacting to a difficult situation faced by 100 million Americans and countless individuals globally: the challenge of managing health treatment for chronic diseases. “Patients are often confused about their care instructions and non-adherence with care plans and medications schedules […]

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