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Training pipelines, evaluation, deployment, and monitoring at scale.
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Why 80% Accuracy Can Mean 0% Found: Accuracy, Precision, Recall and F1 Explained
16+ hour, 16+ min ago (1512+ words) After training a model, you get a few numbers back: accuracy, precision, recall and F1. It is tempting to look at one of them and move on. But a model can score 80% accuracy on a label and still miss every single…...
New Clustering Algorithm Sharpens the Hunt for Data Outliers
16+ hour, 46+ min ago (637+ words) Researchers at Yanshan University have developed ADPOD, an outlier detection algorithm that improves density-peak clustering with an affinity-based density measure, automatic cluster-center selection, and a relative distance metric....
MLOps: models that survive production · 1. Why models fail in production and never in the notebook
1+ day, 2+ hour ago (730+ words) MLOps: models that survive production · Chapter 1 of 12 · AI Engineering · new chapter every Thursday night By the end of this chapter: Name the gap between an experiment and a running system. A machine learning model is the only software artifact where…...
When My House Price Model Hit 100% Accuracy, I Should Have Been Worried
1+ day, 11+ hour ago (538+ words) My first script on the Kaggle House Prices data reported 100% training accuracy. I was pleased for about as long as it took to look at my feature list. The script was meant to predict SalePrice, but I had included SalePrice…...
Visual explainer of empirical Neural Tangent Kernels — LessWrong
1+ day, 12+ hour ago (307+ words) There is a new mech interp method in town. I found it difficult to get my head around at first, but once I understood it, I found it beautiful. I am not an expert in this topic, and mostly write…...
Canary rollouts: upgrade models in production without downtime
2+ day, 6+ hour ago (1733+ words) Introducing Together Link: Run open models inside the harness you already use → The Open Source AI Stack: models, inference, routers, harnesses & tools → 📈 GLM-5.3 vs. GLM 5.3 Flash on DeepSWE → 🚀 DeepSeek V4 Pro 0813 vs. GPT-5.6 Sol on DeepSWE → ⚡ On-demand B200s now available on Together…...
Day 18/60: The Shape of Chaos — Density-Based Clustering & DBSCAN
2+ day, 11+ hour ago (410+ words) Yesterday, we left off with a critical flaw in K-Means. Because K-Means relies on measuring distance from a central point, it fundamentally assumes that all clusters are spherical. If your data forms interlocking rings, crescent moons, or complex winding streams,…...
MLOps and LLMOps: Keep Your AI Reliable After Launch
2+ day, 20+ hour ago (878+ words) MLOps and LLMOps are the monitoring, evaluation, versioning and governance practices that keep AI systems accurate, safe and accountable after go-live. Models drift, prompts regress and costs creep. The fix is a simple loop: instrument every call, evaluate quality on…...
Data Preprocessing in Machine Learning: Encoding and Feature Scaling
2+ day, 22+ hour ago (300+ words) Why your model’s accuracy often depends more on how you prepare the data than on which algorithm you pick. Machine learning models work with numbers. Real-world datasets contain text categories like “Red”, “Mumbai” or “Premium”, and numeric columns on very…...
Someone Trained a Transformer Without Backpropagation. I Rebuilt the Core Trick in Under 60 Lines of NumPy.
3+ day, 2+ hour ago (222+ words) Dust trains a language model using only forward passes and noise. It’s not faster than … Someone Trained a Transformer Without Backpropagation. I Rebuilt the Core Trick in Under 60 Lines of NumPy. Dust trains a language model using only forward passes…...