AWS Machine Learning Blog

Category: SageMaker

Build multiclass classifiers with Amazon SageMaker linear learner

Amazon SageMaker is a fully managed service for scalable training and hosting of machine learning models. We’re adding multiclass classification support to the linear learner algorithm in Amazon SageMaker. Linear learner already provides convenient APIs for linear models such as logistic regression for ad click prediction, fraud detection, or other classification problems, and linear regression […]

Read More

Amazon SageMaker DeepAR now supports missing values, categorical and time series features, and generalized frequencies

Today we are launching several new features for DeepAR in Amazon SageMaker. DeepAR is a supervised machine learning algorithm for time series prediction, or forecasting, that uses recurrent neural networks (RNNs) to produce probabilistic forecasts. Since its launch, the algorithm has been used for a variety of use cases. We are excited to give developers access to new […]

Read More

Amazon SageMaker supports kNN classification and regression

We’re excited to announce that starting today Amazon SageMaker supports a built-in k-Nearest-Neighbor (kNN) algorithm for solving classification and regression problems. kNN is a simple, interpretable, and surprisingly strong model for multi-class classification, ranking, and regression. Introduction to kNN The idea behind kNN is that similar data points should have the same class, at least […]

Read More

Discover Financial Services applies machine learning through a Robocar event powered by Amazon SageMaker

The Discover Financial Services (DFS) team members who attended AWS re:Invent agreed that the Robocar Rally was an extremely impactful experience. By participating in this hackathon, six members of Discover’s core team received hands-on experience using machine learning (ML) and deep learning on AWS. They had a blast and created lasting memories! Discover’s Cloud Center […]

Read More

Secure prediction calls in Amazon SageMaker with AWS PrivateLink

Amazon SageMaker now supports Amazon Virtual Private Cloud (VPC) Endpoints via AWS PrivateLink so you can initiate prediction calls to your machine learning models hosted on Amazon SageMaker inside your VPC, without going over the internet. Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning […]

Read More

Running Principal Component Analysis in Amazon SageMaker

Principal Component Analysis (PCA) is a very popular technique used by data scientists primarily for dimensionality reduction in numerous applications ranging from stock market prediction to medical image classification. Other uses of PCA include de-noising and feature extraction. PCA is also used as an exploratory data analysis tool. To better understand PCA let’s consider an […]

Read More

Build a serverless frontend for an Amazon SageMaker endpoint

Amazon SageMaker provides a powerful platform for building, training, and deploying machine learning models into a production environment on AWS. By combining this powerful platform with the serverless capabilities of Amazon Simple Storage Service (S3), Amazon API Gateway, and AWS Lambda, it’s possible to transform an Amazon SageMaker endpoint into a web application that accepts […]

Read More

Introduction to the Amazon SageMaker Neural Topic Model

Structured and unstructured data are being generated at an unprecedented rate, so you need the right tools to help organize, search, and understand this vast amount of information, it’s challenging to make the data useful. This is especially true for unstructured data, and it’s estimated that over 80% of the data in enterprises is unstructured. Text analytics […]

Read More

Simplify machine learning with XGBoost and Amazon SageMaker

Machine learning is a powerful tool that has recently enabled use cases that were never previously possible–computer vision, self-driving cars, natural language processing, and more. Machine learning is a promising technology, but it can be complex to implement in practice. In this blog post, we explain XGBoost—a machine learning library that is simple, powerful, and […]

Read More

Amazon SageMaker now supports PyTorch and TensorFlow 1.8

Starting today, you can easily train and deploy your PyTorch deep learning models in Amazon SageMaker. This is the fourth deep learning framework that Amazon SageMaker has added support for, in addition to TensorFlow, Apache MXNet, and Chainer.  Just like with those frameworks, now you can write your PyTorch script like you normally would and […]

Read More