AWS Machine Learning Blog

Category: SageMaker

Training models with unequal economic error costs using Amazon SageMaker

Many companies are turning to machine learning (ML) to improve customer and business outcomes. They use the power of ML models built over “big data” to identify patterns and find correlations. Then they can identify appropriate approaches or predict likely outcomes based on data about new instances. However, as ML models are approximations of the […]

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Limit access to a Jupyter notebook instance by IP address

For increased security, Amazon SageMaker customers can now limit access to a notebook instance to a range of IP addresses. IP address filtering helps when you need to allow only a subset of traffic to access your notebook instances. You might want to limit notebook access in the following ways: To comply with security and compliance requirements […]

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Classifying high-resolution chest x-ray medical images with Amazon SageMaker

Medical image processing is one of the key areas where deep learning is applied to great effect. Typical processing involves classification, detection, and segmentation using various medical image modalities. In this blog post, we outline a method to use the HIPAA Eligible service Amazon SageMaker to train a deep learning model for chest x-ray image […]

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Run SQL queries from your SageMaker notebooks using Amazon Athena

The volume, velocity and variety of data has been ever increasing since the advent of the internet. The problem many enterprises face is managing this “big data” and trying to make sense out of it to yield the most desirable outcome. Siloes in enterprises, continuous ingestion of data in numerous formats, and the ever-changing technology […]

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Visual search on AWS—Part 2: Deployment with AWS DeepLens

In Part 1 of this blog post series, we examined use cases for visual search and how visual search works. Now we’ll extend the results of Part 1 from the digital world to the physical world using AWS DeepLens, a deep-learning-enabled video camera. Most current applications of visual search don’t involve direct interaction with the physical […]

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Amazon SageMaker runtime now supports the CustomAttributes header

Amazon SageMaker now supports a new HTTP header for the InvokeEndpoint API action called CustomAttributes which can be used to provide additional information about an inference request or response. Amazon SageMaker strips all POST headers except those supported by the InvokeEndpoint API action and you can use the CustomAttributes header to pass custom information such […]

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Visual search on AWS—Part 1: Engine implementation with Amazon SageMaker

In this two-part blog post series we explore how to implement visual search using Amazon SageMaker and AWS DeepLens. In Part 1, we’ll take a look at how visual search works, and use Amazon SageMaker to create a model for visual search. We’ll also use Amazon SageMaker to build a fast index containing reference items to be searched.

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Access Amazon S3 data managed by AWS Glue Data Catalog from Amazon SageMaker notebooks

In this blog post, I’ll show you how to perform exploratory analysis on massive corporate data sets in Amazon SageMaker. From your Jupyter notebook running on Amazon SageMaker, you’ll identify and explore several corporate datasets in the corporate data lake that seem interesting to you. You’ll discover that each contains a subset of the information you need. You’ll join them to extract the interesting information, then continue analyzing and visualizing your data in your Amazon SageMaker notebook, in a seamless experience.

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New speed record set for training deep learning models on AWS

fast.ai, a research lab dedicated to making deep learning more accessible, has announced that they successfully trained the ResNet-50 deep learning model on a million images in 18 minutes using 16 Amazon EC2 P3.16xlarge instances. They accomplished this milestone by spending just $40. This new speed record illustrates how you can drastically cut down the training times for deep learning models, enabling you to bring your innovations to market faster and at a lower cost.

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Forecasting financial time series with dynamic deep learning on AWS

In this post, I will show you how to develop an original RNN (Recurrent Neural Network) deep learning algorithm to forecast time series based on the past trends of multiple factors, taking advantage of Amazon SageMaker (using Bring-Your-Own-Algorithm). Amazon SageMaker is a fully-managed machine learning platform that enables data scientists and developers to quickly and easily build and train machine learning models into production applications, at scale. It enables you to use both built-in algorithms, built-in frameworks, and also import custom code via Docker containers.

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