Category: SageMaker

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 […]

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 […]

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 […]

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.

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.

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.

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.

Air pollution in cities can be an acute problem leading to damaging effects on people, animals, plants and property. It is an important topic which is getting increased attention as the human population of cities continues to increase. This year it was the subject the 2018 KDD Cup, the annual data mining and knowledge discovery […]

We are very excited to announce that you can deploy your computer vision model trained using TensorFlow (version 1.4) to AWS DeepLens. Head pose detection is part of the AWS DeepLens sample projects. In this blog post, we will show you how to train a model from scratch using a P2 training instance of Amazon […]

All Amazon SageMaker API operations are now fully supported via AWS PrivateLink, which increases the security of data shared with cloud-based applications by reducing data exposure to the internet. In this blog, I show you how to set up a VPC endpoint to secure your Amazon SageMaker API calls using AWS PrivateLink. AWS PrivateLink traffic […]