AWS Machine Learning Blog

Category: SageMaker

Automated and continuous deployment of Amazon SageMaker models with AWS Step Functions

Amazon SageMaker is a complete machine learning (ML) workflow service for developing, training, and deploying models, lowering the cost of building solutions, and increasing the productivity of data science teams. Amazon SageMaker comes with many predefined algorithms. You can also create your own algorithms by supplying Docker images, a training image to train your model […]

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Amazon SageMaker adds Scikit-Learn support

Amazon SageMaker now comes pre-configured with the Scikit-Learn machine learning library in a Docker container. Scikit-Learn is popular choice for data scientists and developers because it provides efficient tools for data analysis and high quality implementations of popular machine learning algorithms through a consistent Python interface and well documented APIs. Scikit-Learn executes quickly and can […]

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Amazon SageMaker Automatic Model Tuning now supports early stopping of training jobs

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 […]

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Anomaly detection on Amazon DynamoDB Streams using the Amazon SageMaker Random Cut Forest algorithm

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 […]

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Amazon SageMaker notebooks now support Git integration for increased persistence, collaboration, and reproducibility

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 […]

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Semantic Segmentation algorithm is now available in Amazon SageMaker

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 […]

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New Features For Amazon SageMaker: Workflows, Algorithms, and Accreditation

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 […]

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Easily monitor and visualize metrics while training models on Amazon SageMaker

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 […]

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Detect suspicious IP addresses with the Amazon SageMaker IP Insights algorithm

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 […]

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Analyze live video at scale in real time using Amazon Kinesis Video Streams and Amazon SageMaker

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 […]

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