Category: Amazon SageMaker

Amazon SageMaker now supports running Batch Transform jobs in Amazon Virtual Private Cloud (Amazon VPC) and using AWS Key Management Service (AWS KMS). Amazon VPC allows you to control access to your machine learning (ML) model containers and data so that they are private and aren’t accessible over the internet. AWS KMS enables you to encrypt […]

Amazon SageMaker now comes with a faster Pipe mode implementation, significantly accelerating the speeds at which data can be streamed from Amazon Simple Storage Service (S3) into Amazon SageMaker while training machine learning models. Pipe mode offers significantly better read throughput than the File mode that downloads data to the local Amazon Elastic Block Store […]

In this blog post, we introduce three new features of the Amazon SageMaker Neural Topic Model (NTM) that are designed to help improve user productivity, enhance topic evaluation capability, and speed up model training. In addition to these new features, by optimizing sparse operations and the parameter server, we have improved the speed of the […]

In Part 1 of this blog post, we demonstrated how to train and deploy neural networks to automatically segment brain tissue from an MRI scan in a simple, streamlined way using Amazon SageMaker. We used Apache MXNet to train a convolutional neural network (CNN) on Amazon SageMaker using the Bring Your Own Script paradigm. We […]

Amazon SageMaker notebook instances provide a scalable cloud based development environment to do data science and machine learning. This blog post will show common workflows to make you more productive and effective. The techniques in this blog post will give you tools to treat your notebook instances in a more cloud native way, remembering that […]

Amazon SageMaker and the AWS Deep Learning AMIs (DLAMI) now provide an easy way to evaluate the PyTorch 1.0 preview release. PyTorch 1.0 adds seamless research-to-production capabilities, while retaining the ease-of-use that has enabled PyTorch to rapidly gain popularity. The AWS Deep Learning AMI comes pre-built with PyTorch 1.0, Anaconda, and Python packages, with CUDA and […]

Annotation and segmentation of medical images is a laborious endeavor that can be automated in part via deep learning (DL) techniques. These approaches have achieved state-of-the-art results in generic segmentation tasks, the goal of which is to classify images at the pixel level. In Part 1 of this blog post, we demonstrate how to train […]

Amazon SageMaker recently released a feature that allows you to automatically tune the hyperparameter values of your machine learning model to produce more accurate predictions. Hyperparameters are user-defined settings that dictate how an algorithm should behave during training. Examples include how large a decision tree should be grown, the number of clusters desired from a […]

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 […]

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 […]