Toyota Research Institute - Advanced Development
Toyota Research Institute Advanced Development, Inc. (TRI-AD) is applying artificial intelligence to help Toyota produce cars in the future that are safer, more accessible and more environmentally friendly. Using PyTorch on Amazon EC2 P3 instances, TRI-AD reduced ML model training time from days to hours. “We continuously optimize and improve our computer vision models, which are critical to TRI-AD’s mission of achieving safe mobility for all with autonomous driving. Our models are trained with PyTorch on AWS, but until now PyTorch lacked a model serving framework. As a result, we spent significant engineering effort in creating and maintaining software for deploying PyTorch models to our fleet of vehicles and cloud servers. With TorchServe, we now have a performant and lightweight model server that is officially supported and maintained by AWS and the PyTorch community,” Yusuke Yachide, Lead of ML Tools at TRI-AD.
Matroid, maker of computer vision software that detects objects and events in video footage, develops a rapidly growing number of machine learning models using PyTorch on AWS and on-premise environments. The models are deployed using a custom model server that requires converting the models to a different format, which is time-consuming and burdensome. TorchServe allows Matroid to simplify model deployment using a single servable file that also serves as the single source of truth, and is easy to share and manage.
Pinterest has 3 billion images and 18 billion associations connecting those images. The company has developed PyTorch deep learning models to contextualize these images and deliver a personalized user experience. Pinterest uses Amazon EC2 P3 instances to speed up model training and deliver low latency inference for an interactive user experience. Read more.
Autodesk, a leader in 3D design, engineering, and entertainment software, uses deep learning models for use cases ranging from exploring thousands of potential design alternatives, semantically searching designs, streamlining engineering construction processes to optimizing rendering workflows. Autodesk uses PyTorch on Amazon SageMaker for developing these models, allowing its researchers to run large-scale experimentation without upfront investment in hardware and infrastructure.
Hyperconnect uses AI-based image classification on its video communication app to recognize the current environment wherein a user is situated. “We reduced our ML model training time from more than a week to less than a day by migrating from on-premises workstations to multiple Amazon EC2 P3 instances using Horovod. In addition, we chose PyTorch as our machine learning framework in order to leverage the libraries available in the open source community thus enabling quick iteration on model development.”
Duolingo provides language learning software with more than 300 million users worldwide. To deliver personalized learning experience, Duolingo created deep learning models using PyTorch that tailor the instructional experience to the student. By using these deep learning models, Duolingo saw an improvement in both prediction accuracy and user engagement. Read more.
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