ML-for-systems research is inherently interdisciplinary and appears in systems, networking, and AI/ML venues. This page provides a starting point (rather than an exhaustive list) for identifying related work and potential target venues. Each link points to a recent edition of the corresponding venue; you may explore past or future editions on your own.
Note that the framing and evaluation norms vary substantially across these communities. Therefore, when reading a paper, consider both its technical contribution and the expectations of the venue in which it appeared.
As a rough guide, systems and networking venues often emphasize end-to-end system design, realistic workloads, implementation quality, and operational impact. AI/ML venues tend to focus on the learning formulation, the choice of baselines, ablation studies, and statistical rigor. We recommend positioning your paper with your target venue in mind.