Call for Papers Industry Track 

Paper submissions due: Jun 7, 2024, 11:59 pm AOE.

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 and/or real-world impact as a result of such deployments
are encouraged.

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DEPLOYED Systems

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.

  • Paper submissions due: Jun 7, 2024, 11:59 pm AOE.
  • Author notifications: Jul 31, 2024, 11:59 pm AOE.
  • Camera ready deadline: Aug 14, 2024, 11:59 pm AOE.
  • Conference dates: Oct 08-11, 2024

Topics of Interest

The topics of interest include AI/ML systems machine learning applications in all mature and emerging domains, as well as contributions to enabling algorithmic, infrastructure, and optimization methodologies to improve learning efficiency, scaling, and adoption/deployment. The topics include, but are not limited to:

 

  • Efficient model training, inference, and serving.
  • Distributed and parallel learning algorithms
  • Privacy and security for ML applications
  • Testing, debugging, and monitoring of ML applications.
  • Fairness, interpretability and explainability for ML applications
  • Data preparation, feature selection, and feature extraction
  • ML programming models and abstractions
  • Programming languages for machine learning
  • Visualization of data, models, and predictions
  • Specialized hardware for machine learning
  • Hardware-efficient ML methods
  • Machine Learning for Systems
  • Systems for Machine Learning
  • Lessons learned from end-to-end production ML pipelines.
  • Emerging practices such as AI-ML Ops
  • Systems for Generative AI
  • Generative AI use-cases
  •  Benchmarking and performance studies for Generative AI tools

Style and Author Instructions

Regular papers must not exceed 6 pages including bibliography.
Short papers must not exceed 4 pages including bibliography and will be presented as posters.
Only electronic submissions in PDF format using the ACM Latex template will be considered. Submissions will be handled through https://cmt3.research.microsoft.com/AIMLSystems2024/.

 We will accept all papers that meet the high quality and innovation levels required by the AI-ML Systems conference. All accepted papers will appear in the proceedings.

 Submission of papers to AI-ML Systems 2024 also carries with it the implied agreement that one or more of the listed authors will register for and attend the conference and present the paper. Papers not presented at the conference will not be included in the final program or in the digital proceedings.