# ml-platform Published articles for ml-platform. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Three principles for building a vector platform at Thumbtack DevFeed: [Three principles for building a vector platform at Thumbtack]() Original publisher: [Read original article]() Author: John Zhu Published: 2026-09-10T15:45:00Z Content type: article Language: en Sources: [Thumbtack Engineering - Medium]() Topics: [Embeddings](), [AI search](), [Database](), [data](), [Machine learning](), [API]() Tags: [api](), [data](), [embeddings](), [engineering](), [etl](), [infrastructure](), [machine-learning](), [ml-platform](), [pgvector](), [retrieval](), [search](), [vector](), [vector-search]() ### AI overview This article explains how Thumbtack built a vector platform that lets ML engineers deploy production vector search without managing database access, custom ETL, or query services. It describes three guiding principles: reuse existing infrastructure, treat embeddings as data, and reduce adoption costs for future teams. ### Source excerpt Reusing what we already had, treating embeddings as data, and lowering the next team's cost Today, an ML engineer at Thumbtack can stand up production vector search without negotiating database access, building a custom ETL, or writing a query service. The team brings their choice of embedding model, the data, and the query; the platform handles what connects them. It took several iterations to get to this point. In this post we'll walk through how we got there and the three principles that shaped what we built. A vector database stores high-dimensional numeric arrays (embeddings) and serves nearest-neighbor queries against them. It's how an ML system asks "what's most similar to this?" instead of "what matches this exact key?" The shift from exact lookup to semantic retrieval is what makes vectors useful: a search can return results that mean the same thing, not just results that spell the same. At Thumbtack, embeddings sit between the models that produce them and the services that consume them: language models for text, multimodal models for images, retrieval models for ranking. The platform we describe here is where those embeddings live and how teams reach for them when they need to. Three principles shaped what we built. Reuse what we have: extend the infrastructure we already run rather than stand up a new system. Treat embeddings as data: flow them through the same pipelines that move every other dataset at the company. Lower the next team's cost: make the platform easier to adopt than to work around. Each principle shaped one layer of the system, and together they took vector search from a one-off project to a platform that any team can build on. Architecture at a glance The platform has four moving parts: where embeddings come from, how they reach the database, where they live, and how consumers query them. Each is a layer, and together they form a pipeline that produces vectors and serves similarity searches as a typed API call. The diagram below traces a ## Issue #1 - The ML platform ghost ship DevFeed: [Issue #1 - The ML platform ghost ship]() Original publisher: [Read original article]() Author: Raphaël Hoogvliets Published: 2024-05-30T17:18:28Z Content type: opinion Language: en Sources: [Raphaël Hoogvliets]() Topics: [Machine learning](), [databricks](), [Data Science](), [CSV]() Tags: [csv](), [data-science](), [databricks](), [ml](), [ml-platform](), [platform]() ### AI overview A consultant recounts a data science project that produced a Databricks ML platform but could not integrate with organizational systems because of blocked IT migrations. The team resorted to manually uploaded CSV files to trigger the pipeline, and the project was eventually abandoned. ### Source excerpt Really? csv-files? 🥲