AWS Machine Learning Blog

Category: Artificial Intelligence

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 […]

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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 […]

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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 […]

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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 […]

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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 […]

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Amazon Transcribe now lets you designate your own Amazon S3 buckets to store transcription outputs  

Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for you to add a speech-to-text capability to your applications. You can use Amazon Transcribe to create text transcripts of audio and video files. Starting today, you can designate your own S3 buckets to store transcription outputs rather than S3 buckets maintained […]

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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 […]

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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 […]

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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 […]

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Building a scalable and adaptable video processing pipeline with Amazon Rekognition Video

This is a guest post by Joe Monti, Sr. Software Engineer at VidMob. Vidmob is, in their own words, “the world’s leading video creation platform, with innovative technology solutions that enable a network of highly trained creators to develop marketing communications that are insight-driven, personalized, and scalable. VidMob creators are trained to produce the full […]

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