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Favorite In just 5 years, tens of thousands of customers have tapped Amazon SageMaker to create millions of models, train models with billions of parameters, and generate hundreds of billions of monthly predictions. The seeds of a machine learning (ML) paradigm shift were there for decades, but with the ready
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Shared by AWS Machine Learning October 26, 2022
Favorite Monitoring machine learning (ML) predictions can help improve the quality of deployed models. Capturing the data from inferences made in production can enable you to monitor your deployed models and detect deviations in model quality. Early and proactive detection of these deviations enables you to take corrective actions, such
Read More Shared by AWS Machine Learning October 26, 2022
Favorite Amazon SageMaker Serverless Inference is a purpose-built inference option that makes it easy for you to deploy and scale machine learning (ML) models. It provides a pay-per-use model, which is ideal for services where endpoint invocations are infrequent and unpredictable. Unlike a real-time hosting endpoint, which is backed by
Read More Shared by AWS Machine Learning October 26, 2022
Favorite Today Amazon SageMaker announced the support of Grid search for automatic model tuning, providing users with an additional strategy to find the best hyperparameter configuration for your model. Amazon SageMaker automatic model tuning finds the best version of a model by running many training jobs on your dataset using
Read More Shared by AWS Machine Learning October 26, 2022
Favorite Posted by Kedem Snir, Software Engineer, and Gal Elidan, Senior Staff Research Scientist, Google Research Whether it’s a professional honing their skills or a child learning to read, coaches and educators play a key role in assessing the learner’s answer to a question in a given context and guiding
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Shared by Google AI Technology October 26, 2022
Favorite Genome sequencing provides a more complete description of cells and organisms, allowing scientists to uncover serious genetic conditions such as the elevated risk for breast cancer or pulmonary arterial hypertension. While researching genomics has the potential to save lives and preserve people’s quality of life, it’s incredibly challenging work.
Read More Shared by Google AI Technology October 26, 2022
Favorite As AI adoption is accelerating across the industry, customers are building sophisticated models that take advantage of new scientific breakthroughs in deep learning. These next-generation models allow you to achieve state-of-the-art, human-like performance in the fields of natural language processing (NLP), computer vision, speech recognition, medical research, cybersecurity, protein
Read More Shared by AWS Machine Learning October 25, 2022
Favorite This post walks you through a few new features that make it simple to design a conversational flow entirely within Amazon Lex that adheres to best practices for IVR design related to retry prompting. We also cover how to configure a DTMF-only prompt as well as other attributes like
Read More Shared by AWS Machine Learning October 25, 2022
Favorite This post was co-written with Sachin Kadyan, a leading developer of OpenFold. In drug discovery, understanding the 3D structure of proteins is key to assessing the ability of a drug to bind to it, directly impacting its efficacy. Predicting the 3D protein form, however, is very complex, challenging, expensive,
Read More Shared by AWS Machine Learning October 25, 2022
Favorite Posted by Rodrigo Benenson, Research Scientist, Google Research Open Images is a computer vision dataset covering ~9 million images with labels spanning thousands of object categories. Researchers around the world use Open Images to train and evaluate computer vision models. Since the initial release of Open Images in 2016,
Read More Shared by Google AI Technology October 25, 2022