LabelSets — open quality standard for AI training data (LQS v3.1) [D]

<!-- SC_OFF --><div class="md"><p>Disclosure: I built LabelSets (labelsets.ai). Sharing the technical approach behind how we score dataset quality.</p> <p>THE PROBLEM</p> <p>Most

<!-- 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>Working on evaluating some AI-generated outbound (SDR-style emails along with follow-ups), and I’m running into a weird problem. Everyone talks

<!-- SC_OFF --><div class="md"><p>Scale AI</p> <p>Highest quality in the industry. But no public pricing and every project requires a sales call. Onboarding takes weeks not days.

<!-- SC_OFF --><div class="md"><p>Hey everyone,</p> <p>I’ve built a fairly flexible RAG pipeline that was initially designed to handle any type of document (PDFs, reports, mixed

<!-- SC_OFF --><div class="md"><p>We’re developing TanML, an MIT-licensed automated model-validation toolkit for tabular machine-learning models.</p> <p>TanML runs locally and

<!-- SC_OFF --><div class="md"><p>Hello everyone.</p> <p>The new dataset is named MONET, is Apache 2.0 and available on HF:</p> <p><a

<!-- SC_OFF --><div class="md"><p>Ps. Not pitching anything; Just trying to understand where reality differs from the narrative</p> <p>We're a couple of ML students, mostly worked