AWS Machine Learning Blog

The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now come with an optimized build of TensorFlow 1.9 custom-built directly from source and fine-tuned for high performance training across Amazon EC2 instances. In addition, the AMIs come with the latest Apache MXNet 1.2 with several performance and usability improvements, the new Keras 2-MXNet backend […]

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 […]

Amazon SageMaker is a fully managed service that supports all of the steps of a ML model’s development: data exploration and building, training, and deploying ML models. With Amazon SageMaker, you can pick and use any of the built-in algorithms, reducing the time to market and the development cost.

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]