Building an operational tool for heavy industry — Seeking "real world" data and

<!-- 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Public datasets on HF or Kaggle can sometimes be too generic, wrong domain, wrong schema, outdated, or just not enough volume to generalize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>Hey 👋</p> <p>I’m building a company <strong>based in the Principality of Monaco</strong>, focused on <strong>private AI for SMEs</strong> —

<!-- 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>I'm trying to understand where people doing sensor based ML on microcontrollers (IMU, accelerometer, vibration ,that kind of time-series data)

<!-- SC_OFF --><div class="md"><p>We’re building a lightweight demand forecasting engine on top of manually entered operational data. No POS integration, no external feeds.

<!-- 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>&#x200b;</p> <p>Hey everyone,</p> <p>I’ve been working on a few real-world projects recently (full-stack + some AI-based ideas), and I’m planning

<!-- SC_OFF --><div class="md"><p>I want to team up on a ML project, no fixed idea yet. Open to whatever's interesting: NLP, CV, time series, whatever you're into.</p> <p>Looking