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Machine learning news and insights

Running on-demand, serverless Apache Spark data processing jobs using Amazon SageMaker managed Spark containers and the Amazon SageMaker SDK

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Apache Spark is a unified analytics engine for large scale, distributed data processing. Typically, businesses with Spark-based workloads on AWS use their own stack built on top of Amazon Elastic Compute Cloud (Amazon EC2), or Amazon EMR to run and scale Apache Spark, Hive, Presto, and other big data frameworks. This is useful for persistent […]


AWS Inferentia is now available in 11 AWS Regions, with best-in-class performance for running object detection models at scale

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AWS has expanded the availability of Amazon EC2 Inf1 instances to four new AWS Regions, bringing the total number of supported Regions to 11: US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Mumbai, Singapore, Sydney, Tokyo), Europe (Frankfurt, Ireland, Paris), and South America (São Paulo). Amazon EC2 Inf1 instances are powered by AWS […]

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

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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 […]



Active learning workflow for Amazon Comprehend custom classification models

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Amazon Comprehend  Custom Classification API enables you to easily build custom text classification models using your business-specific labels without learning ML. For example, your customer support organization can use Custom Classification to automatically categorize inbound requests by problem type based on how the customer has described the issue.  You can use custom classifiers to automatically label […]


Streamline modeling with Amazon SageMaker Studio and the Amazon Experiments SDK

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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 […]








Automating the analysis of multi-speaker audio files using Amazon Transcribe and Amazon Athena

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In an effort to drive customer service improvements, many companies record the phone conversations between their customers and call center representatives. These call recordings are typically stored as audio files and processed to uncover insights such as customer sentiment, product or service issues, and agent effectiveness. To provide an accurate analysis of these audio files, […]