A Technology Broker and Management Company Specializing in KM Systems
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
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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 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 In the past decade, we have seen Deep learning (DL) science adopted at a tremendous pace by AWS customers. The plentiful and jointly trained parameters of DL models have a large representational capacity that brought improvements in numerous customer use cases, including image and speech analysis, natural language processing
Read More Shared by AWS Machine Learning October 24, 2022
Favorite In this post, we introduce a new analysis in the Data Quality and Insights Report of Amazon SageMaker Data Wrangler. This analysis assists you in validating textual features for correctness and uncovering invalid rows for repair or omission. Data Wrangler reduces the time it takes to aggregate and prepare
Read More Shared by AWS Machine Learning October 24, 2022
Favorite Collecting and annotating image data is one of the most resource-intensive tasks on any computer vision project. It can take months at a time to fully collect, analyze, and experiment with image streams at the level you need in order to compete in the current marketplace. Even after you’ve
Read More Shared by AWS Machine Learning October 21, 2022
Favorite Amazon SageMaker and SageMaker inference endpoints provide a capability of training and deploying your AI and machine learning (ML) workloads. With inference endpoints, you can deploy your models for real-time or batch inference. The endpoints support various types of ML models hosted using AWS Deep Learning Containers or your
Read More Shared by AWS Machine Learning October 20, 2022