AWS Machine Learning Blog

Category: AWS Fargate

The following diagram illustrates the solution architecture.

Machine learning on distributed Dask using Amazon SageMaker and AWS Fargate

As businesses around the world are embarking on building innovative solutions, we’re seeing a growing trend adopting data science workloads across various industries. Recently, we’ve seen a greater push towards reducing the friction between data engineers and data scientists. Data scientists are now enabled to run their experiments on their local machine and port to […]

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Building a medical image search platform on AWS

Improving radiologist efficiency and preventing burnout is a primary goal for healthcare providers. A nationwide study published in Mayo Clinic Proceedings in 2015 showed radiologist burnout percentage at a concerning 61% [1]. In additon, the report concludes that “burnout and satisfaction with work-life balance in US physicians worsened from 2011 to 2014. More than half […]

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Visualizing TensorFlow training jobs with TensorBoard

TensorBoard is an open source toolkit for TensorFlow users that allows you to visualize a wide range of useful information about your model, from model graphs; to loss, accuracy, or custom metrics; to embedding projections, images, and histograms of weights and biases. This post demonstrates how to use TensorBoard with Amazon SageMaker training jobs, write […]

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Build a serverless Twitter reader using AWS Fargate

In a previous post, Ben Snively and Viral Desai showed us how to build a social media dashboard using serverless technology. The social media dashboard reads tweets with the #AWS hashtag, uses machine learning based services to do translation, and natural language processing (NLP) to determine topics, entities, and sentiment analysis. Finally, it aggregates this […]

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