AWS Machine Learning Blog

Category: Artificial Intelligence

Moving from notebooks to automated ML pipelines using Amazon SageMaker and AWS Glue

A typical machine learning (ML) workflow involves processes such as data extraction, data preprocessing, feature engineering, model training and evaluation, and model deployment. As data changes over time, when you deploy models to production, you want your model to learn continually from the stream of data. This means supporting the model’s ability to autonomously learn […]

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BERT inference on G4 instances using Apache MXNet and GluonNLP: 1 million requests for 20 cents

Bidirectional Encoder Representations from Transformers (BERT) [1] has become one of the most popular models for natural language processing (NLP) applications. BERT can outperform other models in several NLP tasks, including question answering and sentence classification. Training the BERT model on large datasets is expensive and time consuming, and achieving low latency when performing inference […]

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Data visualization and anomaly detection using Amazon Athena and Pandas from Amazon SageMaker

Many organizations use Amazon SageMaker for their machine learning (ML) requirements and source data from a data lake stored on Amazon Simple Storage Service (Amazon S3). The petabyte scale source data on Amazon S3 may not always be clean because data lakes ingest data from several source systems, such as like flat files, external feeds, […]

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Football tracking in the NFL with Amazon SageMaker

With the 2020 football season kicking off, Amazon Web Services (AWS) is continuing its work with the National Football League (NFL) on several ongoing game-changing initiatives. Specifically, the NFL and AWS are teaming up to develop state-of-the-art cloud technology using machine learning (ML) aimed at aiding the officiating process through real-time football detection. As a […]

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Improved OCR and structured data extraction with Amazon Textract

Optical character recognition (OCR) technology, which enables extracting text from an image, has been around since the mid-20th century, and continues to be a research topic today. OCR and document understanding are still vibrant areas of research because they’re both valuable and hard problems to solve. AWS has been investing in improving OCR and document […]

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Preventing customer churn by optimizing incentive programs using stochastic programming

In recent years, businesses are increasingly looking for ways to integrate the power of machine learning (ML) into business decision-making. This post demonstrates the use case of creating an optimal incentive program to offer customers identified as being at risk of leaving for a competitor, or churning. It extends a popular ML use case, predicting […]

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Selecting the right metadata to build high-performing recommendation models with Amazon Personalize

In this post, we show you how to select the right metadata for your use case when building a recommendation engine using Amazon Personalize. The aim is to help you optimize your models to generate more user-relevant recommendations. We look at which metadata is most relevant to include for different use cases, and where you […]

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Streamline modeling with Amazon SageMaker Studio and the Amazon Experiments SDK

The modeling phase is a highly iterative process in machine learning (ML) projects, where data scientists experiment with various data preprocessing and feature engineering strategies, intertwined with different model architectures, which are then trained with disparate sets of hyperparameter values. This highly iterative process with many moving parts can, over time, manifest into a tremendous […]

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Expanding Amazon Lex conversational experiences with US Spanish and British English

Amazon Lex provides the power of automatic speech recognition (ASR) for converting speech to text, along with natural language understanding (NLU) for recognizing user intents. This combination allows you to develop sophisticated conversational interfaces using both voice and text for chatbots, IVR bots, and voicebots. This week, we’re announcing Amazon Lex support for British English […]

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Gaining insights into winning football strategies using machine learning

University of Illinois, Urbana Champaign (UIUC) has partnered with the Amazon Machine Learning Solutions Lab to help UIUC football coaches prepare for games more efficiently and improve their odds of winning. Previously, coaches prepared for games by creating a game planning sheet that only featured types of plays for a certain down and distance, and […]

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