Natural Language Assessment: A New Framework to Promote Education
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 them towards a goal. These interactions …
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Amazon SageMaker Automatic Model Tuning now supports grid search
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 a range of hyperparameters that you …
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Introducing the Amazon SageMaker Serverless Inference Benchmarking Toolkit
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 a long-running instance, compute resources for …
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Deploy a machine learning inference data capture solution on AWS Lambda
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 as retraining models, auditing upstream systems, …
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AWS Celebrates 5 Years of Innovation with Amazon SageMaker
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 availability of virtually infinite compute capacity, …
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Improved TabNet on Vertex AI: High-performance, scalable Tabular Deep Learning
Data scientists choose models based on various tradeoffs when solving machine learning (ML) problems that involve tabular (i.e., structured) data, the most common data type within enterprises. Among such models, decision trees are popular because they are easy to interpret, fast to train, and can obtain high accuracy quickly from small-scale datasets. On the other …
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Open Images V7 — Now Featuring Point Labels
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, which included image-level labels covering 6k …
Run inference at scale for OpenFold, a PyTorch-based protein folding ML model, using Amazon EKS
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, and time consuming, and can take …
Configure DTMF slots and ordered retry prompts with Amazon Lex
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 timeouts and barge-in. When designing an …
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