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Favorite Machine learning (ML) has proven to be one of the most successful and widespread applications of technology, affecting a wide range of industries and impacting billions of users every day. With this rapid adoption of ML into every industry, companies are facing challenges in supporting low-latency predictions and with
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Shared by AWS Machine Learning October 31, 2022
Favorite With environmental, social, and governance (ESG) initiatives becoming more important for companies, our customer, one of Greater China region’s top convenience store chains, has been seeking a solution to reduce food waste (currently over $3.5 million USD per year). Doing so will allow them to not only realize substantial
Read More Shared by AWS Machine Learning October 27, 2022
Favorite This post is co-written with Chaim Rand from Mobileye. Certain machine learning (ML) workloads, such as training computer vision models or reinforcement learning, often involve combining the GPU- or accelerator-intensive task of neural network model training with the CPU-intensive task of data preprocessing, like image augmentation. When both types
Read More Shared by AWS Machine Learning October 27, 2022
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
Read More 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 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