Favorite For data scientists and machine learning (ML) developers, data preparation is one of the most challenging and time-consuming tasks of building ML solutions. In an often iterative and highly manual process, data must be sourced, analyzed, cleaned, and enriched before it can be used to train an ML model.

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Shared by AWS Machine Learning May 15, 2021

Favorite This is the second in a two-part series on the Amazon SageMaker Ground Truth hierarchical labeling workflow and dashboards. In Part 1: Automate multi-modality, parallel data labeling workflows with Amazon SageMaker Ground Truth and AWS Step Functions, we looked at how to create multi-step labeling workflows for hierarchical label

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Shared by AWS Machine Learning May 14, 2021

Favorite Machine learning (ML) is realized in inference. The business problem you want your ML model to solve is the inferences or predictions that you want your model to generate. Deployment is the stage in which a model, after being trained, is ready to accept inference requests. In this post,

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Shared by AWS Machine Learning May 14, 2021

Favorite Neural networks have proven effective at solving complex computer vision tasks such as object detection, image similarity, and classification. With the evolution of low-cost GPUs, the computational cost of building and deploying a neural network has drastically reduced. However, most techniques are designed to handle pixel resolutions commonly found

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Shared by AWS Machine Learning May 13, 2021

Favorite Whether you’re watching a live broadcast of your favorite soccer team, having a video chat with a vendor, or calling your bank about a loan payment, streaming speech content is everywhere. You can apply a streaming transcription service to generate subtitles for content understanding and accessibility, to create metadata

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Shared by AWS Machine Learning May 12, 2021

Favorite Medical data is highly contextual and heavily multi-modal, in which each data silo is treated separately. To bridge different data, a knowledge graph-based approach integrates data across domains and helps represent the complex representation of scientific knowledge more naturally. For example, three components of major electronic health records (EHR)

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Shared by AWS Machine Learning May 12, 2021

Favorite A common problem in manufacturing is verifying that products meet quality standards. You can use manual inspection on a subset of the products, but it’s usually not scalable enough to meet demand as production grows. In this post, I go through the steps of creating an end-to-end machine vision

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Shared by AWS Machine Learning May 11, 2021

Favorite AWS is excited to announce that The Washington Post is integrating Amazon Polly to provide their readers with audio access to stories across The Post’s entire spectrum of web and mobile platforms, starting with technology stories. Amazon Polly is a service that turns text into lifelike speech, allowing you to create applications that

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Shared by AWS Machine Learning May 11, 2021

Favorite Unstructured data belonging to the enterprise continues to grow, making it a challenge for customers and employees to get the information they need. Amazon Kendra is a highly accurate intelligent search service powered by machine learning (ML). It helps you easily find the content you’re looking for, even when

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Shared by AWS Machine Learning May 8, 2021

Favorite This is the first in a two-part series on the Amazon SageMaker Ground Truth hierarchical labeling workflow and dashboards. In Part 1, we look at creating multi-step labeling workflows for hierarchical label taxonomies using AWS Step Functions. In Part 2 (coming soon), we look at how to build dashboards

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Shared by AWS Machine Learning May 6, 2021