Edge Inference on Unknown Models at Adobe Target

Georgiana Copil, Iulian Radu, and Akash Maharaj, Adobe

Abstract: 

Customer’s Data Scientist: “I know much better my business, just give me the data and I’ll create the model you should run.” Transforming this into reality, in our production systems, comes with a lot of challenges, which we will discuss in this talk.

In today’s world, increasingly many companies build their own data science/ML departments. When needing to run their custom models on different systems, the models need to be converted to other frameworks or to a format interpretable in representation standards for machine learning models (e.g., ONNX). In this talk we discuss challenges and approaches to using such models in real-time, low-latency systems. We discuss the limitations of existing frameworks, scoring runtimes, model representations, and the existing solutions to overcome them. We discuss how these methods can be used today to build a solution that provides real-time scoring for high throughput workload.

Georgiana Copil, Adobe

Georgiana Copil is a computer scientist working on Adobe Target, on the experience selection engine for Adobe's Experience Cloud. Before that, she worked as a consultant for SAP on innovation projects, and as university assistant at Vienna University of Technology (TU Wien), on research projects with focus on applying machine learning models to cloud elasticity. With a PhD in informatics from TU Wien, she is currently focusing on topics on the edge of machine learning, distributed systems, and operations.

Iulian Radu, Adobe

Iulian Radu is a senior computer scientist working on Adobe Target. In the past he's been part of the big data processing team for Adobe Audience Manager where he experimented with running machine learning and data processing jobs at scale. Before that, he wrote compilers, applied machine learning algorithms for game AI and played with image processing and object recognition algorithms.

Akash Maharaj, Adobe

Akash Maharaj is a senior data scientist working on Adobe Target, the experience selection engine for Adobe's Experience Cloud. For the past three yars, he has worked on improving the quality and speed of several recommendations and personalization algorithms that power Adobe Target's B2B solutions. Before that, he completed his PhD in physics at Stanford.

OpML '20 Open Access Sponsored by NetApp

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

BibTeX
@conference {256652,
author = {Georgiana Copil and Iulian Radu and Akash Maharaj},
title = {Edge Inference on Unknown Models at Adobe Target},
year = {2020},
publisher = {{USENIX} Association},
month = jul,
}

Presentation Video 
Teaser
Full