Favorite 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
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Shared by AWS Machine Learning September 29, 2020
Favorite 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
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Shared by AWS Machine Learning September 29, 2020
Favorite 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
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Shared by AWS Machine Learning September 29, 2020
Favorite 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
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Shared by AWS Machine Learning September 26, 2020
Favorite 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
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Shared by AWS Machine Learning September 26, 2020
Favorite 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
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Shared by AWS Machine Learning September 26, 2020
Favorite My favourite definition of Knowledge Management is the current ISO definition – “Management with a focus on knowledge”. But what activities does this management involve? RSiegel week 3 by Rebecca Siegel on Flickr I took an online collection of 65 KM definitions (now no longer available I am afraid)
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Shared by Nick Milton September 25, 2020
Favorite 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
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Shared by AWS Machine Learning September 25, 2020
Favorite 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,
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Shared by AWS Machine Learning September 25, 2020
Favorite 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
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Shared by AWS Machine Learning September 25, 2020
Shared by AWS Machine Learning September 29, 2020
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