• ai@iferp.net
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Sponsorship Brochure

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 expect that this conference will come out excited and exhausted, wanting more. Sure will get a better understanding of how to build intelligent applications, learn and observer how others are using intelligent techniques. This will introduce new tools and techniques, which can improve workflow, or induce to start AI & Machine learning career, ultimatum is to result in global wellbeing.

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.

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