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In this post, we show how to train, deploy, and predict natural disaster damage with Amazon SageMaker with geospatial capabilities. We use the new SageMaker geospatial capabilities to generate new inference data to test the model. Many government and humanitarian organizations need quick and accurate situational awareness when a disaster strikes. Knowing the severity, cause, …
Amazon SageMaker Autopilot automatically builds, trains, and tunes the best machine learning (ML) models based on your data, while allowing you to maintain full control and visibility. Autopilot can also deploy trained models to real-time inference endpoints automatically. If you have workloads with spiky or unpredictable traffic patterns that can tolerate cold starts, then deploying …
Read more “Deploy Amazon SageMaker Autopilot models to serverless inference endpoints”
Imagine trying to teach a toddler what a unicorn is. A good place to start might be by showing the child images of the creature and describing its unique features. Now imagine trying to teach an artificially intelligent machine what a unicorn is. Where would one even begin? Pretrained AI models offer a solution. A …
Posted by Krishna Giri Narra, Software Engineer, Google, and Chiyuan Zhang, Research Scientist, Google Research Ad technology providers widely use machine learning (ML) models to predict and present users with the most relevant ads, and to measure the effectiveness of those ads. With increasing focus on online privacy, there’s an opportunity to identify ML algorithms …
Posted by Malaya Jules, Program Manager, Google This week, the premier conference on Empirical Methods in Natural Language Processing (EMNLP 2022) is being held in Abu Dhabi, United Arab Emirates. We are proud to be a Diamond Sponsor of EMNLP 2022, with Google researchers contributing at all levels. This year we are presenting over 50 …
In previous blog post, we described an end-to-end identity verification solution in a single AWS Region. The solution uses the Amazon Rekognition APIs DetectFaces for face detection and CompareFaces for face comparison. We think of those APIs as stateless APIs because they don’t depend on an Amazon Rekognition face collection. They’re also idempotent, meaning repeated …
Read more “Improve scalability for Amazon Rekognition stateless APIs using multiple regions”
The success of any machine learning (ML) pipeline depends not just on the quality of model used, but also the ability to train and iterate upon this model. One of the key ways to improve an ML model is by choosing better tunable parameters, known as hyperparameters. This is known as hyperparameter optimization (HPO). However, …
Editor’s note: This post is part of an ongoing series on IT predictions from Google Cloud experts. Check out the full list of our predictions on how IT will change in the coming years. Prediction: AI will be the primary driver for moving to a 4-day work week Enterprise use of artificial intelligence (AI) has …
Read more “IT prediction: AI could help realize the dream of the four-day work week”
Training, testing and validating autonomous vehicles requires a continuous pipeline — or data factory — to introduce new scenarios and refine deep neural networks. A key component of this process is simulation. AV developers can test a virtually limitless number of scenarios, repeatably and at scale, with high-fidelity, physically based simulation. And like much of …
Read more “Hittin’ the Sim: NVIDIA’s Matt Cragun on Conditioning Autonomous Vehicles in Simulation”
This paper was accepted at the workshop “Learning from Time Series for Health” at NeurIPS 2022. Heart rate (HR) dynamics in response to workout intensity and duration measure key aspects of an individual’s fitness and cardiorespiratory health. Models of exercise physiology have been used to characterize cardiorespiratory fitness in well-controlled laboratory settings, but face additional …
Read more “Modeling Heart Rate Response to Exercise with Wearable Data”