AWS Machine Learning Blog
Amazon SageMaker Neo Enables Pioneer’s Machine Learning in Cars
Pioneer Corp is a Japanese multinational corporation specializing in digital entertainment products. Pioneer wanted to help their customers check road and traffic conditions through in-car navigation systems. They developed a real-time, image-sharing service to help drivers navigate. The solution analyzes photos, diverts traffic, and sends alerts based on the observed conditions. Because the pictures are of public roadways, they also had to ensure privacy by blurring out faces and license plate numbers.
Pioneer built their image-sharing service using Amazon SageMaker Neo. Amazon SageMaker is a fully-managed service that provides the ability for developers to build, train, and deploy machine learning models at much less effort and lower cost. Amazon SageMaker Neo is a service that allows developers to train machine learning models once and run them anywhere in the cloud and at the edge. Amazon SageMaker Neo optimizes models to run up to twice as fast, with less than a tenth of the memory footprint, with no loss in accuracy.
You start with an ML model built using MXNet, TensorFlow, PyTorch, or XGBoost and trained using Amazon SageMaker. Then, choose your target hardware platform such as M4/M5/C4/C5 instances or edge devices. With a single click, Amazon SageMaker Neo compiles the trained model into an executable.
The compiler uses a neural network to discover and apply all of the specific performance optimizations to make your model run most efficiently on the target hardware platform. You can deploy the model to start making predictions in the cloud or at the edge.
At launch, Amazon SageMaker Neo was available in four AWS Regions: US East (N. Virginia), US West (Oregon), EU (Ireland), Asia Pacific (Seoul). As of May 2019, SageMaker Neo is now available in Asia Pacific (Tokyo), Japan.
Pioneer developed a machine learning model for real-time image detection and classification using data from cameras in cars. They detect many different kinds of images, such as license plates, people, street traffic, and road signs. The in-car cameras upload data to the cloud and run inference using Amazon SageMaker Neo. The results are sent back to the cars so drivers can be informed on the road.
Here’s how it works.
“We decided to use Amazon SageMaker, a fully managed service for machine learning,” said Ryunosuke Yamauchi, an AI Engineer at Pioneer. “We needed a fully managed service because we didn’t want to spend time managing GPU instances or integrating different applications. In addition, Amazon SageMaker offers hyperparameter optimization, which eliminates the need for time-consuming, manual hyperparameter tuning. Also, we choose Amazon SageMaker because it supports all leading frameworks such as MXNet GluonCV. That’s our preferred framework because it provides state-of-the-art pre-trained object detection models such as Yolo V3.”
To learn more about Amazon SageMaker Neo, see the Amazon SageMaker Neo webpage.
About the Authors
Satadal Bhattacharjee is Principal Product Manager with AWS AI. He leads the Machine Learning Engine PM team working on projects such as SageMaker Neo, AWS Deep Learning AMIs, and AWS Elastic Inference. For fun outside work, Satadal loves to hike, coach robotics teams, and spend time with his family and friends.
Kimberly Madia is a Principal Product Marketing Manager with AWS Machine Learning. Her goal is to make it easy for customers to build, train, and deploy machine learning models using Amazon SageMaker. For fun outside work, Kimberly likes to cook, read, and run on the San Francisco Bay Trail.