Featureform is a virtual feature store. It enables data scientists to define, manage, and serve their ML model's features. Featureform sits atop your existing infrastructure and orchestrates it to work like a traditional feature store. By using Featureform, a data science team can solve the organizational problems:. Source: almost 2 years ago

Usually embeddings — dense numerical representations of real-world objects and relationships, expressed as a vector — are stored in database servers such as PostgreSQLEmbedding. However Embeddinghub makes it easier to store your embeddings and load them. You can get started with minimal setup, and it also makes your code look less verbose as compared to, say, building a KNN model using scikit-learn. - Source: dev.to / about 2 years ago

There are already ways to get around this. For example, renting compute from people who aren't in datacenters. Which is already a thing: https://vast.ai. - Source: Hacker News / 4 months ago

By "SETI" I assume you mean the SETI@Home distributed computing project. There's a two-way market where you can rent out your GPU here: https://vast.ai/. - Source: Hacker News / 5 months ago

- https://vast.ai/ (linked by gchadwick above). - Source: Hacker News / 6 months ago

Have you considered running on a cloud machine instead? You can rent machines on https://vast.ai/ for under $1 an hour that should work for small/medium models (I've mostly been playing with stable diffusion so I don't know what you'd need for an LLM off hand). Good GPUs and Apple hardware is pricey. Get a bit of automation setup with some cloud storage (e.g backblaze B2) and you can have a machine ready to run... - Source: Hacker News / 6 months ago

I have heard vast.ai is cheap but I haven't tried it out. https://websiteinvesting.com/reviews/vast-ai-review/. Source: 6 months ago