Category: 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.

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 […]

April 2023 Update: Starting January 31, 2024, you will no longer be able to access AWS DeepLens through the AWS management console, manage DeepLens devices, or access any projects you have created. To learn more, refer to these frequently asked questions about AWS DeepLens end of life. We are very excited to announce that you can […]

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 […]

The Amazon SageMaker pre-built MXNet container now uses the latest release of Apache MXNet 1.2.  Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.  And the pre-built MXNet container makes it easy to write your deep learning scripts naturally […]

Not only does Amazon SageMaker  provide easy scalability and distribution to train and host ML models, it is modularized so that the process of training a model is decoupled from deploying the model. This means that models that are trained outside of Amazon SageMaker can be brought into SageMaker only to be deployed. This is very useful […]

Supervised learning needs labels, or annotations, that tell the algorithm what the right answers are in the training phases of your project. In fact, many of the examples of using MXNet, TensorFlow, and PyTorch start with annotated data sets you can use to explore the various features of those frameworks. Unfortunately, when you move from […]