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Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML

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Re: Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML

#71

Post by bratao »

orderedtree wrote: post #50
bratao wrote: post #19

Thrilled to hear about MosaicML's successful exit! Nonetheless, this could serve as an indication to explore other options. Given Databricks' past acquisition of Redash which led to its downfall, there's no absolute assurance that Mosaic's fate won't be similar.

Why do you say it led to redash's downfall? What happened to redash since the acquisition?

They've essentially killed Redash, which was one of the best open-source dashboard/data visualization tools. They had promised to keep it open-source and improve upon it (https://web.archive.org/web/20211019150919/https://blog.reda...).

However, a year later, their SaaS has been discontinued. The open-source repository is now stagnant, with hundreds of unresolved merge requests. On a positive note, they recently shared the repository with some other open-source maintainers, so there's hope that Redash will be reborn.

Re: Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML

#72

Post by chrisjc »

cfeduke wrote: post #53
ramraj07 wrote: post #36

Earlier quoted context omitted.

You can’t be more wrong? Snowflake also stores in s3 or elsewhere, in fact it’s like the only common feature. Snowflake uses proprietary formats mostly though and only gives a sql interface. Think of it as Apple vs android.

> You can’t be more wrong? Snowflake also stores in s3 or elsewhere Snowflake stores data in S3 in Snowflake's various AWS accounts. Egress fees are necessary when performing anything outside of Snowflake. Databricks operates on data stored in your S3 on your AWS account(s). Databricks also runs on your compute contained within your AWS account(s). Both approaches have valid use cases.

> Snowflake stores data in S3 in Snowflake's various AWS accounts. Egress fees are necessary when performing anything outside of Snowflake.

Egress fees are only if you are unloading into a different region/cloud. If your data is in SF AWS us-east-1, and you unload to your S3 in us-east-1, there is no egress fee.

> Snowflake charges a per-byte fee for data egress when users transfer data from a Snowflake account into a different region on the same cloud platform or into a completely different cloud platform. Data transfers within the same region are free.

https://docs.snowflake.com/en/user-guide/cost-understanding-...

Re: Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML

#73

Post by george_123 »

quickthrower2 wrote: post #62
stingraycharles wrote: post #3

I can’t help but feel this is mostly hype driven by a company that’s looking to reinvent themselves while facing the prospect of irrelevance. The piece reads like it’s a press release, with abstract things such as “corporate leaders under pressure to get their data ready for AI”. That has nothing to do with LLMs, as the current AI “hype” cycle has been going on for almost a decade already. Corporate leaders have alwa…

Looking at the Mosaic website, and looking at many AI startups, I can't help feeling like they are all small AWS Sagemaker, Bedrock, Trainium, ... (etc.) competitors, and not sure how they will compete with the sheer capital that Amazon has, and potential to offer that compute power cheaper (at least long enough to kill off most of the competition). Maybe it is it the off chance that one of these companies might beco…

A lot of AWS services (especially SageMaker) aren’t very good in customer experience. People buy them for nominal capabilities and AWS core bread and butter — short-term and long-term reliability.

Most of these startups (AI and others) have to offer a compelling product before even being notable.

Besides, AWS top level doesn’t care if you use sagemaker or not. There’s a premium but if you’re still using EC2 via another startup, they’re still capturing lions share of value.

Re: Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML

#74

Post by ml_hardware »

brucethemoose2 wrote: post #37

Falcon 7B/40B is good, but hasn't really caught on like LLaMA has. One big reason is that its not well supported by 4-bit inference code (namely llama.cpp and GPTQ). Mosaic's 7B (and 30B?) models have the same issue, and 7B kinda paled in comparison to LLaMA 7B... But maybe it would be better if finetuned? To me, its kinda baffling that Mosaic didn't work on adding highly quantized inference to the popular frameworks…

Mosaic's MPT models are already supported in GGML: https://github.com/ggerganov/ggml

Here's MPT-30B running in 4-bit precision on CPU :) https://twitter.com/abacaj/status/1673133443339763712?s=20

Re: Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML

#75

Post by DebtDeflation »

anonylizard wrote: post #4
stingraycharles wrote: post #3

I can’t help but feel this is mostly hype driven by a company that’s looking to reinvent themselves while facing the prospect of irrelevance. The piece reads like it’s a press release, with abstract things such as “corporate leaders under pressure to get their data ready for AI”. That has nothing to do with LLMs, as the current AI “hype” cycle has been going on for almost a decade already. Corporate leaders have alwa…

A rising tide lifts all boats. Databricks may have its thunder stolen by snowflake. But the true AI boom happening right now, benefits many data product vendors. The basic requirement for enterprises to use LLMs is having their data in order, which basically requires a cloud data warehouse. It is simply responsible to profit off the hype for a established data company.

> The basic requirement for enterprises to use LLMs is having their data in order, which basically requires a cloud data warehouse.

I'm curious why you would say this. The main use cases for LLMs involve things like customer service chatbots, knowledgebase search, document summarization, co-pilot/code generation, and content generation for product descriptions and marketing emails. The main data sources would be things like document repositories on Sharepoint and transcripts of customer calls. Not a bunch of historical sales data or financial data sitting in the typical data warehouse. I think there's a big misconception about this. Data sources for Generative AI are very different than data sources for 2013 era data science projects (which perhaps not coincidentally is what led to the development of things like Databricks).

Re: Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML

#76

Post by esafak »

disgruntledphd2 wrote: post #58
aynyc wrote: post #51

Earlier quoted context omitted.

Their moat was to be able to spin up spark clusters for you. This was very useful when companies just starting in that space and don’t have the skills. But with K8s and clusters becoming more common for normal workload, it’s not hard to manage your own spark clusters anymore. There goes their moat. Their other offerings just aren’t that amazing. People now don’t just want to buy hand tools anymore, they want power to…

Their moat is their phenomenal sales team. Like, I'm at company 3 that pays for databricks. Amount of those companies that use Spark = 0. I've given up complaining now.

So what are they using, the notebooks?

Re: Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML

#77

Post by dbish »

george_123 wrote: post #73
quickthrower2 wrote: post #62

Earlier quoted context omitted.

Looking at the Mosaic website, and looking at many AI startups, I can't help feeling like they are all small AWS Sagemaker, Bedrock, Trainium, ... (etc.) competitors, and not sure how they will compete with the sheer capital that Amazon has, and potential to offer that compute power cheaper (at least long enough to kill off most of the competition). Maybe it is it the off chance that one of these companies might beco…

A lot of AWS services (especially SageMaker) aren’t very good in customer experience. People buy them for nominal capabilities and AWS core bread and butter — short-term and long-term reliability. Most of these startups (AI and others) have to offer a compelling product before even being notable. Besides, AWS top level doesn’t care if you use sagemaker or not. There’s a premium but if you’re still using EC2 via anoth…

Sagemaker is probably a bad example since most folks who run ML workloads on AWS don't use it (from what I've seen talking to many ML teams). It's partially a scattered focus on their customer type (are they for experts/non-experts/something in between?, what exact use cases are they covering?) and partially I think just bad UI/UX (related to customer focus). AWS will converge on what is working for ML platforms and just build their own as time goes on. First mover advantage won't matter and these earlier ML platforms will be consumed imho

Re: Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML

#78

Post by justinclift »

bratao wrote: post #71
orderedtree wrote: post #50

Earlier quoted context omitted.

Why do you say it led to redash's downfall? What happened to redash since the acquisition?

They've essentially killed Redash, which was one of the best open-source dashboard/data visualization tools. They had promised to keep it open-source and improve upon it ( https://web.archive.org/web/20211019150919/https://blog.reda... ). However, a year later, their SaaS has been discontinued. The open-source repository is now stagnant, with hundreds of unresolved merge requests. On a positive note, they recently sh…

> On a positive note, they recently shared the repository with some other open-source maintainers, so there's hope that Redash will be reborn.

Yeah, the root of the problem (in my opinion) was that Redash has sooo many Python dependencies (due to supporting so many databases and similar) that it's become a real hairball source code wise to keep them all playing together nicely.

Especially as over time (say) library package FOO has some security vulnerability reported that gets fixed in a new release... but the dependencies of the newer release are too new to work with (say) package BAR. Times that by 50 and it's a real pita.

Simultaneously to that, the Redash team got busy with their work at Databricks (mostly not Redash related). Then the automatic CircleCI checks on PRs started failing (ugh), etc.

---

But, as @bratao mentioned above that's all getting worked through now.

Admin and maintainer permissions have been given to a group of dev volunteers / known Redash enthusiasts. CI is working again now (as of last night), and we're currently untangling the dependency hairball.

It's likely to be a few weeks (minimum I guess) before any new official releases are ready, but it will happen. :)

Re: Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML

#79

Post by fakedang »

stingraycharles wrote: post #3

I can’t help but feel this is mostly hype driven by a company that’s looking to reinvent themselves while facing the prospect of irrelevance. The piece reads like it’s a press release, with abstract things such as “corporate leaders under pressure to get their data ready for AI”. That has nothing to do with LLMs, as the current AI “hype” cycle has been going on for almost a decade already. Corporate leaders have alwa…

Just curious, how is Databricks going to be irrelevant? I thought they were a reasonably powerful database provider for data analytics platforms in many companies?

Re: Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML

#80

Post by esafak »

tsunamifury wrote: post #39
Zizizizz wrote: post #22

Earlier quoted context omitted.

Databricks provides Jupyter lab like notebooks for analysis and ETL pipelines using spark through pyspark, sparkql or scala. I think R is supported as well but it doesn't interop as well with their newer features as well as python and SQL do. It interfaces with cloud storage backend like S3 and offers some improvements to the parquet format of data querying that allows for updating, ordering and merged through https:…

Now say it even dumber as if I were a dog or a ceo.

They create services for data scientists (notebooks) and machine learning engineers (Spark).
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