Category: Amazon SageMaker

Data scientists and developers can now easily train machine learning models on datasets labeled by Amazon SageMaker Ground Truth. Amazon SageMaker Training now accepts the labeled datasets produced in augmented manifest format as input through both AWS Management Console and Amazon SageMaker Python SDK APIs. Last month during AWS re:Invent, we launched Amazon SageMaker Ground […]

In June 2018, we launched Amazon SageMaker Automatic Model Tuning, a feature that automatically finds well-performing hyperparameters to train a machine learning model with. Unlike model parameters learned during training, hyperparameters are set before the learning process begins. A typical example of the use of hyperparameters is the learning rate of stochastic gradient procedures. Using […]

Have you considered introducing anomaly detection technology to your business? Anomaly detection is a technique used to identify rare items, events, or observations which raise suspicion by differing significantly from the majority of the data you are analyzing.  The applications of anomaly detection are wide-ranging including the detection of abnormal purchases or cyber intrusions in […]

It’s now possible to associate GitHub, AWS CodeCommit, and any self-hosted Git repository with Amazon SageMaker notebook instances to easily and securely collaborate and ensure version-control with Jupyter Notebooks. In this blog post, I’ll elaborate on the benefits of using Git-based version-control systems and how to set up your notebook instances to work with Git repositories. Data […]

Amazon SageMaker is a managed and infinitely scalable machine learning (ML) platform. With this platform, it is easy to build, train, and deploy machine learning models. Amazon SageMaker already has two popular built-in computer vision algorithms for image classification and object detection. The Amazon SageMaker image classification algorithm learns to categorize images into a set of […]

We’ve seen a ton of progress in machine learning during the past 12 months, with customers using Amazon SageMaker – a fully-managed service which has put ML into the hands of tens of thousands of developers and data scientists – to find fraud, predict pitches, and tune engines. We’ve added nearly 100 new features and […]

Data scientists and developers can now quickly and easily access, monitor, and visualize metrics that are computed while training machine learning models on Amazon SageMaker. You can now specify the metrics you want to track by using the AWS Management Console for Amazon SageMaker or by using the Amazon SageMaker Python SDK APIs. After the […]

Today, we are announcing the new IP Insights algorithm for Amazon SageMaker. IP Insights is an unsupervised learning algorithm for detecting anomalous behavior and usage patterns of IP addresses. In this blog post, we introduce the problem of identifying fraudulent behavior using IP addresses, describe the Amazon SageMaker IP Insights algorithm, demonstrate how you can use it in […]

We are excited to announce the launch of the Amazon Kinesis Video Streams Inference Template (KIT) for Amazon SageMaker. This capability enables customers to attach Kinesis Video streams to Amazon SageMaker endpoints in minutes. This drives real-time inferences without having to use any other libraries or write custom software to integrate the services. The KIT comprises […]

Earlier this year, we launched Amazon SageMaker Automatic Model Tuning, which allows developers and data scientists to save significant time and effort in training and tuning their machine learning models. Today, we are launching warm start of hyperparameter tuning jobs in Automatic Model Tuning. Data scientists and developers can now create a new hyperparameter tuning […]