Today, I’m extremely happy to announce Amazon SageMaker Edge Manager, a new capability of Amazon SageMaker that makes it easier to optimize, secure, monitor, and maintain machine learning models on a fleet of edge devices.
Edge computing is certainly one of the most exciting developments in information technology. Indeed, thanks to continued advances in compute, storage, networking, and battery technology, organizations routinely deploy large numbers of embedded devices anywhere on the planet for a wide range of industry applications: manufacturing, energy, agriculture, healthcare, and more. Ranging from simple sensors to large industrial machines, the devices have a common purpose: capture data, analyze it, and act on it, for example send an alert if an unwanted condition is detected.
As machine learning (ML) demonstrated its ability to solve a wide range of business problems, customers tried to apply it to edge applications, training models in the cloud and deploying them at the edge in an effort to extract deeper insights from local data. However, given the remote and constrained nature of edge devices, deploying and managing models at the edge is often quite difficult.
For example, a complex model can be too large to fit, forcing customers to settle for a smaller and less accurate model. Also, predicting with several models on the same device (say, to detect different types of anomalies) may require additional code to load and unload models on demand, in order to conserve hardware resources. Finally, monitoring prediction quality is a major concern, as the real world will always be more complex and unpredictable than any training set can anticipate.
Customers asked us to help them solve these challenges, and we got to work.
Announcing Amazon SageMaker Edge Manager
Amazon SageMaker Edge Manager makes it easy for ML edge developers to use the same familiar tools in the cloud or on edge devices. It reduces the time and effort required to get models to production, while continuously monitoring and improving model quality across your device fleet.
Starting from a model that you trained or imported in Amazon SageMaker, SageMaker Edge Manager first optimizes it for your hardware platform using Amazon SageMaker Neo. Launched two years ago, Neo converts models into an efficient common format which is executed on the device by a low footprint runtime. Neo currently supports devices based on chips manufactured by Ambarella, ARM, Intel, NVIDIA, NXP, Qualcomm, TI, and Xilinx.
Then, SageMaker Edge Manager packages the model, and stores it in Amazon Simple Storage Service (S3), where it can be deployed to your devices. In fact, you can deploy multiple models, loading and predicting with a runtime optimized for your hardware of choice.
On-device models are managed by the SageMaker Edge Manager Manager Agent, which communicates with the AWS Cloud for model deployment, and with your application for model management. Indeed, you can integrate this agent with your application, so that it may automatically load and unload models according to your prediction requests. This enables a variety of scenarios, such as freeing all resources for a large model whenever needed, or working with a collection of smaller models that cohabit in memory.
Lenovo, the #1 global PC maker, recently incorporated Amazon SageMaker into its latest predictive maintenance offering. Igor Bergman, Lenovo Vice President, Cloud & Software of PCs and Smart Devices, told us: “At Lenovo, we’re more than a hardware provider and are committed to being a trusted partner in transforming customers’ device experience and delivering on their business goals. Lenovo Device Intelligence is a great example of how we’re doing this with the power of machine learning, enhanced by Amazon SageMaker. With Lenovo Device Intelligence, IT administrators can proactively diagnose PC issues and help predict potential system failures before they occur, helping to decrease downtime and increase employee productivity. By incorporating Amazon SageMaker Neo, we’ve already seen a substantial improvement in the execution of our on-device predictive models – an encouraging sign for the new Amazon SageMaker Edge Manager that will be added in the coming weeks. SageMaker Edge Manager will help eliminate the manual effort required to optimize, monitor, and continuously improve the models after deployment. With it, we expect our models will run faster and consume less memory than with other comparable machine learning platforms. As we extend AI to new applications across the Lenovo services portfolio, we will continue to require a high-performance pipeline that is flexible and scalable both in the cloud and on millions of edge devices. That’s why we selected the Amazon SageMaker platform. With its rich edge-to-cloud and CI/CD workflow capabilities, we can effectively bring our machine learning models to any device workflow for much higher productivity.”
Getting Started
As you can see, SageMaker Edge Manager makes it easier to work with ML models deployed on edge devices. It’s available today in the US East (N. Virginia), US West (Oregon), US East (Ohio), Europe (Ireland), Europe (Frankfurt), and Asia Pacific (Tokyo) regions.
Sample notebooks are available to get you started right away. Give them a try, and let us know what you think.
We’re always looking forward to your feedback, either through your usual AWS support contacts, or on the AWS Forum for SageMaker.
