AWS Machine Learning Blog

Scalable multi-node deep learning training using GPUs in the AWS Cloud 

A key barrier to the wider adoption of deep neural networks on industrial-size datasets is the time and resources required to train them. AlexNet, which won the 2012 ImageNet Large Scale Visual Recognition Competition (ILSVRC) and kicked off the current boom in deep neural networks, took nearly a week to train across the 1.2-million-image, 1000-category […]

Call an Amazon SageMaker model endpoint using Amazon API Gateway and AWS Lambda

December 2022: This post was reviewed and updated for accuracy. At AWS Machine Learning (ML) workshops, customers often ask, “After I deploy an endpoint, where do I go from there?” You can deploy an Amazon SageMaker trained and validated ML model as an online endpoint in production. Alternatively, you can choose which SageMaker functionality to […]

Amazon Comprehend now supports Syntax Analysis

We’re excited to announce that Amazon Comprehend now provides a Syntax API.  This enables you to tokenize text (for example, to extract word boundaries) and the corresponding part of speech (PoS) for each word. Today, Amazon Comprehend enables analysis use cases like such as knowing whether a customer comment is negative or positive, and identifying […]

Create a model for predicting orthopedic pathology using Amazon SageMaker

Artificial intelligence (AI) and machine learning (ML) are gaining momentum in the healthcare industry, especially in healthcare imaging. The Amazon SageMaker approach to ML presents promising potential in the healthcare field. ML is considered a horizontal enabling layer applicable across industries. Within healthcare, this can serve analogous to a radiology or lab report as a […]

Enhanced text classification and word vectors using Amazon SageMaker BlazingText

Today, we are launching several new features for the Amazon SageMaker BlazingText algorithm. Many downstream natural language processing (NLP) tasks like sentiment analysis, named entity recognition, and machine translation require the text data to be converted into real-valued vectors. Customers have been using BlazingText’s highly optimized implementation of the Word2Vec algorithm, for learning these vectors from […]

Object Detection algorithm now available in Amazon SageMaker

Amazon SageMaker is a fully-managed and highly scalable machine learning (ML) platform that makes it easy build, train, and deploy machine learning models. This is a giant step towards the democratization of ML and in lowering the bar for entry in to the ML space for developers. Computer vision is the branch of machine learning […]

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 […]

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 […]

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 […]