AI and ML models are moving to the edge for inferencing. If the public cloud is the preferred environment for training the models, it is the edge where most of the models are deployed for inferencing. Unlike the cloud and data center, edge computing environments are highly constrained. They are deployed in remote, harsh environments that make them difficult to manage. To optimize deep learning models for the edge, both hardware and software vendors have built various platforms to accelerate the inferencing. This session introduces the current state of AI@Edge while discussing various choices from Amazon, Google, Intel, Microsoft, NVIDIA, and Qualcomm.

One emerging trend that's going to fundamentally change the face of ML is AutoML. It enables business analysts and developers to evolve machine learning models that can address complex scenarios. From platform companies such as Google and Microsoft to early-stage startups, AutoML is fast gaining traction. This session demonstrates how AutoML accelerates building machine learning models.