4 ms·

I don't know if I'd say this is reinventing themselves, or at least not from the ground up, they've been somewhat active in the open source LLM scene for some t

I don't know if I'd say this is reinventing themselves, or at least not from the ground up, they've been somewhat active in the open source LLM scene for some time now. Not that releasing dolly alone caused some major foundational shift, but it was a solid contribution. Additionally, acquiring Mosaic fills a pretty gaping hole in their existing product offering. The whole process of pretraining, fine-tuning, and evaluating open source LLMs in production is a total nightmare currently. Mosaic's stack at least attempts to make that better (on top of releasing solid foundational models themselves). > “corporate leaders under pressure to get their data ready for AI”. That has nothing to do with LLMs I agree that its buzzwordy and a little abstract. But also in my experience, getting "data ready for AI" is actually the primary constraint many orgs have with respect to using LLMs in an enterprise context. Their data is not stored in a way thats easy to tokenize/label/embed for effective training or fine-tuning. And you could argue the preprocessing actually should the easy part for non-ML devs to tackle, as its primarily a software engineering problem. Yet, its still the thing that keeps many folks from getting started (before you even get to attempt to tackle the inference problem).

ethbr0 3y ago
From my understanding, Databrick's core value proposition is -- 'We let you do things you want to, with the people you have.' It may surprise HN to learn, but most companies don't have top-tier technical talent. Consequently, how do you do {cutting edge thing board and investors are demanding company do} with the people you have/can afford? Use an offering that decreases the necessary skill level by providing powerful prebuilt components. From this perspective, LLM integration sounds like a perfect fit. It's something every company is being asked to have a plan on, but one few are technically staffed to execute on their own.
- stakhanov 3y ago
  > [...] LLM integration [...] is something every company is being asked to have a plan on, but one few are technically staffed to execute on their own. But this is still the "language of hype". LLM integration to do what? I'm not saying they're not doing anything useful, just that this is not a way to describe either a productivity-enhancing technology (feature) or an actual value proposition (benefit) in a meaningful way.
  - antonvs 3y ago
    > this is not how you describe a meaningful value proposition. It depends who you're selling to. If customers are asking for a way to deal with LLMs, then providing that is a perfectly fine value proposition. The "do what" is going to depend on the customer.
  - ethbr0 3y ago
    Investors don't understand AI past "It will change things." Which means there needs to be a plan to deal with change. What the end deliverable of the plan is doesn't matter: effectively it's the company demonstrating it can "do AI," so that if it becomes existentially necessary, the company's ability to execute is already known. Absent demonstration, hell, the company might not even be retaining useful historical data. (E.g. theft data at a major US retailer)
    - stakhanov 3y ago
      Well, there was a time when making XML-related software would have been a driver of valuations of companies in a similar way, and engineers were asked by boards to "start doing XML". I'm not even saying it's unmitigatedly a negative thing. When the question "What are we going to actually do with XML?" came up, many had good ideas, and useful tech investments started getting past boards that wouldn't otherwise have. -- So there was a lot of "collateral goodness". But people also started ripping out databases and purpose-built DSLs from existing applications just so they could replace them with something XML-based. As a way of making tech investments, it's weak. If it's 1999 and I'm asked to invest in a tech company that makes a damned good XML parser and nothing else, I'm well advised to not invest. And if I'm a CTO in a non-tech business asked to start replacing database servers with XML files, I'm well advised to not do it.
- ordinaryperson 3y ago
  Databricks' core value proposition is Apache Spark in the cloud, optimized with their special sauce (eg Proton engine). Running your own Spark, especially on prem, is a lot of work. Most companies would prefer to just provide their data and let someone else handle the query engine. The parent is right however that Databricks has a feature store (tokenization) but it's not simple to set up and just getting content in and out of it is a major pain point right now.

ethbr0 3y ago

From my understanding, Databrick's core value proposition is -- 'We let you do things you want to, with the people you have.' It may surprise HN to learn, but most companies don't have top-tier technical talent. Consequently, how do you do {cutting edge thing board and investors are demanding company do} with the people you have/can afford? Use an offering that decreases the necessary skill level by providing powerful prebuilt components. From this perspective, LLM integration sounds like a perfect fit. It's something every company is being asked to have a plan on, but one few are technically staffed to execute on their own.

stakhanov 3y ago
> [...] LLM integration [...] is something every company is being asked to have a plan on, but one few are technically staffed to execute on their own. But this is still the "language of hype". LLM integration to do what? I'm not saying they're not doing anything useful, just that this is not a way to describe either a productivity-enhancing technology (feature) or an actual value proposition (benefit) in a meaningful way.
- antonvs 3y ago
  > this is not how you describe a meaningful value proposition. It depends who you're selling to. If customers are asking for a way to deal with LLMs, then providing that is a perfectly fine value proposition. The "do what" is going to depend on the customer.
- ethbr0 3y ago
  Investors don't understand AI past "It will change things." Which means there needs to be a plan to deal with change. What the end deliverable of the plan is doesn't matter: effectively it's the company demonstrating it can "do AI," so that if it becomes existentially necessary, the company's ability to execute is already known. Absent demonstration, hell, the company might not even be retaining useful historical data. (E.g. theft data at a major US retailer)
  - stakhanov 3y ago
    Well, there was a time when making XML-related software would have been a driver of valuations of companies in a similar way, and engineers were asked by boards to "start doing XML". I'm not even saying it's unmitigatedly a negative thing. When the question "What are we going to actually do with XML?" came up, many had good ideas, and useful tech investments started getting past boards that wouldn't otherwise have. -- So there was a lot of "collateral goodness". But people also started ripping out databases and purpose-built DSLs from existing applications just so they could replace them with something XML-based. As a way of making tech investments, it's weak. If it's 1999 and I'm asked to invest in a tech company that makes a damned good XML parser and nothing else, I'm well advised to not invest. And if I'm a CTO in a non-tech business asked to start replacing database servers with XML files, I'm well advised to not do it.
ordinaryperson 3y ago
Databricks' core value proposition is Apache Spark in the cloud, optimized with their special sauce (eg Proton engine). Running your own Spark, especially on prem, is a lot of work. Most companies would prefer to just provide their data and let someone else handle the query engine. The parent is right however that Databricks has a feature store (tokenization) but it's not simple to set up and just getting content in and out of it is a major pain point right now.