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Favorite Machine learning (ML) models have long been considered black boxes because predictions from these models are hard to interpret. However, recently, several frameworks aiming at explaining ML models were proposed. Model interpretation can be divided into local and global explanations. A local explanation considers a single sample and answers
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Shared by AWS Machine Learning November 4, 2020
Favorite Amazon Kendra is a highly accurate and easy-to-use intelligent search service powered by machine learning (ML). Amazon Kendra provides native connectors for popular data sources like Amazon Simple Storage Service (Amazon S3), SharePoint, ServiceNow, OneDrive, Salesforce, and Confluence so you can easily add data from different content repositories and
Read More Shared by AWS Machine Learning November 4, 2020
Favorite High-quality machine learning (ML) models depend on accurately labeled, high-quality training, validation, and test data. As ML and deep learning models are increasingly integrated into production environments, it’s becoming more important than ever to have customizable, real-time data labeling pipelines that can continuously receive and process unlabeled data. For
Read More Shared by AWS Machine Learning November 3, 2020
Favorite There have been breakthroughs in understanding COVID-19, such as how soon an exposed person will develop symptoms and how many people on average will contract the disease after contact with an exposed individual. The wider research community is actively working on accurately predicting the percent population who are exposed,
Read More Shared by AWS Machine Learning October 31, 2020
Favorite Every day there is something new going on in the world of AWS Machine Learning—from launches to new to use cases to interactive trainings. We’re packaging some of the not-to-miss information from the ML Blog and beyond for easy perusing each month. Check back at the end of each
Read More Shared by AWS Machine Learning October 31, 2020
Favorite Model training and serving steps are two essential pieces of a successful end-to-end machine learning (ML) pipeline. These two steps often require different software and hardware setups to provide the best mix for a production environment. Model training is optimized for a low-cost, feasible total run duration, scientific flexibility,
Read More Shared by AWS Machine Learning October 31, 2020
Favorite Based on our day to day experience, the information we consume is entirely digital. We read the news on our mobile devices far more than we do from printed copy newspapers. Tickets for sporting events, music concerts, and airline travel are stored in apps on our phones. One could
Read More Shared by AWS Machine Learning October 31, 2020
Favorite Conversational interfaces like chatbots have become an important channel for brands to communicate with their customers, partners, and employees. They offer faster service, 24/7 availability, and lower service costs. By analyzing your bot’s customer conversations, you can discover challenges in user experience, trending topics, and missed utterances. These additional
Read More Shared by AWS Machine Learning October 30, 2020
Favorite Many builders and teams on AWS use Confluence as a way of collaborating and sharing information within their teams and across their organizations. These types of workspaces are rich with data and contain sets of knowledge and information that can be a great source of truth to answer organizational
Read More Shared by AWS Machine Learning October 29, 2020
Favorite Amazon Kinesis Video Streams allows you to easily ingest video data from connected devices for processing. One of the most effective ways to process this video data is using the power of deep learning. You can create an efficient service infrastructure to run these computations with a Java server,
Read More Shared by AWS Machine Learning October 28, 2020