Category: Amazon SageMaker

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 […]

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 […]

Data scientists and developers can use the Amazon SageMaker fully managed machine learning service to build and train machine learning (ML) models, and then directly deploy them into a production-ready hosted environment. In this blog post we’ll show you  how to use Amazon SageMaker to do transfer learning using a TensorFlow container with our own […]

Amazon SageMaker is a fully managed service that supports all of the steps of a ML model’s development: data exploration and building, training, and deploying ML models. With Amazon SageMaker, you can pick and use any of the built-in algorithms, reducing the time to market and the development cost.

At AWS Machine Learning workshops, customers often ask, “After I deploy an endpoint, where do I go from there?” You can deploy an Amazon SageMaker trained and validated machine learning model as an endpoint in production. Alternatively, you can choose which Amazon SageMaker functionality to use. For example, you could choose just to train a […]

Artificial intelligence (AI) and machine learning (ML) are gaining momentum in the healthcare industry, especially in healthcare imaging. The Amazon SageMaker approach to ML presents promising potential in the healthcare field. ML is considered a horizontal enabling layer applicable across industries. Within healthcare, this can serve analogous to a radiology or lab report as a […]