<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine-Learning on Sohil Ladhani Blog</title><link>https://sohilladhani.com/blog/tags/machine-learning/</link><description>Recent content in Machine-Learning on Sohil Ladhani Blog</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 22 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://sohilladhani.com/blog/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Feature Stores</title><link>https://sohilladhani.com/blog/post/2026-04-22-feature-stores/</link><pubDate>Wed, 22 Apr 2026 00:00:00 +0000</pubDate><guid>https://sohilladhani.com/blog/post/2026-04-22-feature-stores/</guid><description>You train a model using yesterday&amp;rsquo;s data. You serve it using today&amp;rsquo;s data. The feature computation logic is slightly different between the two. The model degrades silently and you spend a week figuring out why.
The Training-Serving Skew Problem ML models are trained on offline batches: historical data, features computed via Spark jobs, labels aggregated over time. At serving time, features are computed online: live data, lower latency budget, different code path.</description></item><item><title>Embedding Vectors and ANN Search</title><link>https://sohilladhani.com/blog/post/2026-04-21-embedding-vectors-and-ann-search/</link><pubDate>Tue, 21 Apr 2026 00:00:00 +0000</pubDate><guid>https://sohilladhani.com/blog/post/2026-04-21-embedding-vectors-and-ann-search/</guid><description>&amp;ldquo;Find the 10 most similar items to this one&amp;rdquo; sounds simple. With millions of items represented as 256-dimensional vectors, exact search is too slow to be useful in production.
What Embeddings Are An ML model maps an item (a product, a document, a user&amp;rsquo;s history) to a dense numeric vector. The geometry of that vector space encodes semantic similarity: similar items land close together. You train the model on interaction data and the embeddings learn to represent &amp;ldquo;things that users treat similarly.</description></item><item><title>Collaborative Filtering</title><link>https://sohilladhani.com/blog/post/2026-04-20-collaborative-filtering/</link><pubDate>Mon, 20 Apr 2026 00:00:00 +0000</pubDate><guid>https://sohilladhani.com/blog/post/2026-04-20-collaborative-filtering/</guid><description>You don&amp;rsquo;t know what a user wants. But you know what people like them have wanted. That&amp;rsquo;s the intuition behind collaborative filtering.
The Two Approaches User-based CF finds users similar to you, then recommends what they liked. Item-based CF finds items similar to what you&amp;rsquo;ve already liked. Item-based is generally more stable because user behavior shifts rapidly (you might buy a couch once), while item similarity changes slowly (a couch is similar to other furniture regardless of who buys it).</description></item></channel></rss>