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Training pipelines, evaluation, deployment, and monitoring at scale.
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Canary rollouts: upgrade models in production without downtime
2+ hour, 33+ min 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
8+ hour, 8+ min 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
17+ hour, 18+ min 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
19+ hour, 13+ min 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.
23+ hour, 4+ min 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
1+ day, 9+ 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)
1+ day, 18+ 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…...
Production MMM: Why Uncalibrated Models Fail and How Meridian 2.1.0 Solves It
2+ day, 2+ hour ago (297+ words) Marketing measurement is fundamentally a causal inference problem constrained by observational data. The core challenge is observational confounding. Historical ad spend is rarely randomized; rather, ad pacing is strictly endogenous to expected demand. Advertisers increase budgets during high-conversion periods, creating…...
How Accurate Is Your Model Right Now? Estimating Accuracy Without Labels
2+ day, 10+ hour ago (272+ words) Label-free accuracy estimation under data shift — and the monitoring sidecar that uses it. Your model was 91% accurate in validation. Three months later, nobody knows what it is. Labels are expensive, slow, and often weeks away. Drift dashboards show you input…...
Aggregating Decoder in Transformer Model
3+ day, 17+ hour ago (537+ words) From the previous experiment makes me think how the important of the identity of the original token embedding vector. Which x_0 is token embedding without any transformation and f is a decoder function. Compare to the Transformer model in general, this…...