Embedded/edge ML folks: what actually eats the most time ,getting data, or clean

<!-- SC_OFF --><div class="md"><p>Public datasets on HF or Kaggle can sometimes be too generic, wrong domain, wrong schema, outdated, or just not enough volume to generalize

<!-- SC_OFF --><div class="md"><p>I've been using AI extensively for ML research, and I've noticed a bottleneck: brainstorming research directions and generating smoke run

<!-- SC_OFF --><div class="md"><p>Just went through a Stanford paper that tracked 51 actual AI deployments - not surveys or sentiment polls, real production systems across 41

<!-- SC_OFF --><div class="md"><p>doing infrastructure evaluation for inference workloads and running into the same problem everywhere: every platform publishes p50 cold start

<!-- SC_OFF --><div class="md"><p>Hi, <a href="/r/MachineLearning">r/MachineLearning</a>: has much research been done in large-scale training scenarios where undesirable data has

<!-- SC_OFF --><div class="md"><p>I'm building a crowdsourced photo collection platform </p> <p>(contributors take photos with smartphones, we auto-label </p> <p>with YOLO/CLIP +

<!-- SC_OFF --><div class="md"><p>Hi everyone,</p> <p>I’m part of a small team currently in the R&amp;D phase of building a new tool for industrial operations (specifically

<!-- SC_OFF --><div class="md"><p>Been thinking about this a lot lately. Almost every analytics platform claims to support real-time processing in 2026, but most define real-time