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Training pipelines, evaluation, deployment, and monitoring at scale.
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MLOps: models that survive production · 1. Why models fail in production and never in the notebook
9+ hour, 5+ min 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
18+ hour, 26+ min 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
18+ hour, 53+ min 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
1+ day, 12+ 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
1+ day, 18+ 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, 3+ 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, 5+ 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.
2+ day, 9+ 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…...
Hypergraph Adapters Push Parameter-Efficient Multimodal Fine-Tuning to New Heights
2+ day, 19+ hour ago (30+ words) Researchers have introduced HGA-Net, a hypergraph-based adapter that connects related examples within each mini-batch and achieves near-perfect fine-grained recognition with only 1.573 million trainable parameters on a frozen CLIP backbone....
Reshape vs View in PyTorch: What's the Difference (and When `view()` Breaks)
3+ day, 4+ hour ago (401+ words) A tensor's data and its shape are two separate things. The data is a flat row of numbers sitting in memory; the shape is just the lens you look at it through. Reshaping changes the lens without touching the numbers…...