With the rapid growth of industrial and real-life adoption of artificial intelligence (AI) and machine learning (ML), a new research area is emerging at their intersection with systems design. This area is seeded by the continued growth in data volume, rapid increase in size and complexity of predictive models and scale-up supported through development of large-scale AI/ML hardware. We solicit submissions of papers describing designs and implementations of solutions and systems for practical tasks at the intersection of AI/ML and computer systems. The primary emphasis is on papers that ​either solve or advance the understanding of ​issues related to deploying learning systems in the real world. We also aim to elicit new connections among these diverse fields, and identify tools, best practices and design principles. Papers demonstrating ​significant, verifiable​ business- or real-world impact as a result of such deployments are encouraged.

We specially encourage implementation of a system that solves a real-world problem and is (or was or is planned) in production use for an extended period. The paper should present the problem, its significance to the application domain, the decisions and tradeoffs made when making design choices for the solution, the deployment challenges, and the lessons learned from successes and failures (when applicable). Papers that describe enabling infrastructure for large-scale deployment of applied machine learning also fall in this category. The work may particularly focus on how to overcome real challenges in the pipelines which may include data collection, low-resource processing, and usability, and it is perfectly fine that the underlying machine learning algorithms are not fundamentally groundbreaking.