AWS Machine Learning Blog

Category: SageMaker

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 […]

Read More

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 […]

Read More

Amazon SageMaker Automatic Model Tuning becomes more efficient with warm start of hyperparameter tuning jobs

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 […]

Read More

Introduction to Amazon SageMaker Object2Vec 

In this blog post, we’re introducing the Amazon SageMaker Object2Vec algorithm, a new highly customizable multi-purpose algorithm that can learn low dimensional dense embeddings of high dimensional objects. Embeddings are an important feature engineering technique in machine learning (ML). They convert high dimensional vectors into low-dimensional space to make it easier to do machine learning […]

Read More

K-means clustering with Amazon SageMaker

Amazon SageMaker provides several built-in machine learning (ML) algorithms that you can use for a variety of problem types. These algorithms provide high-performance, scalable machine learning and are optimized for speed, scale, and accuracy. Using these algorithms you can train on petabyte-scale data. They are designed to provide up to 10x the performance of the other […]

Read More

Now easily perform incremental learning on Amazon SageMaker

Data scientists and developers can now easily perform incremental learning on Amazon SageMaker. Incremental learning is a machine learning (ML) technique for extending the knowledge of an existing model by training it further on new data. Starting today both of the Amazon SageMaker built-in visual recognition algorithms – Image Classification and Object Detection – will […]

Read More

Direct access to Amazon SageMaker notebooks from Amazon VPC by using an AWS PrivateLink endpoint

Amazon SageMaker now supports AWS PrivateLink for notebook instances. In this post, I will show you how to set up AWS PrivateLink to secure your connection to Amazon SageMaker notebooks. Maintaining compliance with regulations such as HIPAA or PCI may require preventing information from traversing the internet. Additionally, preventing exposure of data to the public internet reduces the likelihood […]

Read More

Customize your notebook volume size, up to 16 TB, with Amazon SageMaker

Amazon SageMaker now allows you to customize the notebook storage volume when you need to store larger amounts of data. Allocating the right storage volume for your notebook instance is important while you develop machine learning models. You can use the storage volume to locally process a large dataset or to temporarily store other data to work with. […]

Read More

Lifecycle configuration update for Amazon SageMaker notebook instances

Amazon SageMaker now allows customers to update or disassociate lifecycle configurations for notebook instances with the renewed APIs. You can associate, switch between, or disable lifecycle configurations as necessary by stopping your notebook instance and using the UpdateNotebookInstance API at any point of the notebook instance’s lifespan. Lifecycle configurations are handy when you want to organize and automate the setup that is […]

Read More

Now use Pipe mode with CSV datasets for faster training on Amazon SageMaker built-in algorithms

Amazon SageMaker built-in algorithms now support Pipe mode for fetching datasets in CSV format from Amazon Simple Storage Service (S3) into Amazon SageMaker while training machine learning (ML) models. With Pipe input mode, the data is streamed directly to the algorithm container while model training is in progress. This is unlike File mode, which downloads […]

Read More