Half of shipped models run blind: only 48% of teams monitor performance. Not an AI capability problem, an ops problem. #MLOps

"87% of ML projects fail" has no primary source. The real number is worse: only 22% of ambitious ML ever ships. #MLOps

Your model isn't the product. The plumbing is. Google drew it in 2015: ML code is a tiny box in a sea of pipelines. #MLOps

Forecasting "foundation models" promise zero-shot magic. On real ops data they lost to a naive baseline. Stay skeptical. #Forecasting

When ML finally won a forecasting contest, the weapon wasn't deep learning. It was LightGBM. Every top-50 team used it. #DataScience

M5 contest, Walmart data: 92.5% of teams couldn't beat simple exponential smoothing. Beat naive before you deep-learn. #Forecasting

99% of ML lives on rung 1 of Pearl's ladder: correlation. Every real decision is an intervention. Harder question. #CausalInference

Berkeley 1973: 44% of men admitted, 35% of women. Looked like bias. Split by department, it flips. Correlation isn't cause. #CausalInference

Your 0.95-AUC model answers a question you never asked. Accuracy predicts who buys. Decisions need who buys because of you. #CausalInference

~6% of ImageNet's labels are wrong. On the fixed set, ResNet-18 beats ResNet-50. A decade of ranking on a broken ruler. #DataScience

TabPFN beat a 4-hour tuned model in 2.8 seconds (Nature 2025). The punchline: to ship it, they distill it back into trees. #DataScience

Neural nets don't lose on tabular data by accident. They lose by design: they assume smooth functions. Business data isn't. #MachineLearning

The fraud flag on your card isn't a chatbot. It's gradient-boosted trees on a spreadsheet, not a transformer. #MachineLearning

Is your enterprise LLM stack optimized for production? Run a free 3-minute architecture diagnostic. Get your cost audit, hallucination risk score, and latency breakdown instantly. Try it here: allerin.com/ai-architecture-… #EnterpriseAI #RAG

We focus on three things: 1. Agentic AI systems (agents that work with guardrails, not chaos) 2. Computer vision (99.4% accuracy on manufacturing floors) 3. Enterprise analytics (turning data into decisions, not dashboards)

Some numbers from real deployments: Claims processing: 14 days to 2 days Visual inspection: 94% human accuracy to 99.4% AI accuracy Scrap reduction: 2-3% down to under 1% Throughput increase: 35% more output, same machines

If you're stuck between "AI demo that impressed the board" and "AI system that actually runs in production" - that's exactly where we live. DMs are open. Or just follow along. We share what we learn here every day. #ProductionAI #EnterpriseAI

We're Allerin. We deploy production AI for enterprises. Not demos. Not proofs of concept. Not "AI strategies." Actual systems running in production, making decisions, saving money. Here's what we do and why it matters. A thread.

What makes us different: We deploy in 4-8 weeks. Not 6-12 months. Every system ships with a kill switch, audit trail, and rollback capability. Because enterprise AI isn't about being impressive. It's about being trustworthy.

The accuracy compounding problem nobody talks about: If your AI agent is 85% accurate per step (sounds great, right?) A 10-step workflow succeeds only 20% of the time. 85% ^ 10 = 19.7% This is why #ProductionAI is a completely different discipline than building a demo.