Matt Turck

Kedro, Quantumblack Labs | Python Software Engineer | London | REMOTE currently, ONSITE ? | Full-time Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code. It borrows concepts from software engineering best-practice and applies them to machine-learning code; applied concepts include modularity, separation of concerns and versioning. Kedro is listed on the 2020 ThoughtWorks Technology Radar (https://mattturck.com/data2020/) and has been framed as the "React for Data Science". Check out our repository: https://github.com/quantumblacklabs/kedro Please apply here mentioning Kedro + HN in your application: https://rb.gy/1njxa1 We are at an exciting juncture in the maturing ML and MLOps space. We are keen to expand our function set through more sophisticated deployments and integrations with other ecosystem tools.

That's a really neat way to brainstorm. Let me add one more thing. The big companies have an advantage in hardware and research. But they dont care about niche applications of their tech, because prizes worth less than $1B don't matter at their scale. That's where I try to focus on. The key challenge is data. Too many AI startups get stuck in the "give us your data and we'll do some awesome stuff." That almost never works. [This](http://mattturck.com/2016/09/29/building-an-ai-startup/) talk does a really good job explaining why. The trick is figuring out how to get enough initial data to deliver value upfront.

(disclaimer: I'm a founder in this space) > the project is either so incomplete or so lacking in a central design that the best thing to do is replace the old system I put a lot of blame here on "the modern data stack". Hundreds[0] of point-solution data tools and very few of them achieve "business outcomes" on their own. You need to stitch together 5 of them to get dashboards, 7 of them to get real time analytics, etc. We're going to see more products that achieve an outcome end-to-end. A lot of companies just want a few dashboards that give a 360 degree view of their data. They want all their data in one spot, an easy way to access it and don't want to spend fortune on it. That's what we're focused on at Definite[1]. We're built on the best open source data projects (e.g. DuckDB, Iceberge, Cube, etc.). If you decide to self host, you can use the same components, but it's generally cheaper to use us than manage all this stuff yourself. 0 - https://mattturck.com/landscape/mad2024.pdf 1 - https://www.definite.app/ 2 - https://youtu.be/7FAJLc3k2Fo

Here's such a VC: https://mattturck.com/landscape/mad2024.pdf

Related big data landscape 2020: https://mattturck.com/data2020/

I would say that the FSF is using Free Software as a weapon to get all the other proprietary software to open up. Usually the biggest barriers to entry by free software are network effects and proprietary agreements. See the recent article about Bloomberg http://mattturck.com/2014/03/19/can-the-bloomberg-terminal-be-toppled/ (I bet someone at bloomberg read it because they released an article the next day: http://www.bloombergview.com/articles/2014-03-20/tesla-can-topple-the-…

Dave does a nice job of articulating some of the forces changing VC. We are a data company that sells data to many of the top VCs so here are some observations based on how we see them work. First, there are a good # of VCs who are evolving. Not sure they are trying to scale in the way Dave talks about, but they're evolving how they run their business. We do see many using data in very interesting and systematic ways – analyzing industry trends including financing flows & exits, improving their deal flow management practices, understanding investment patterns of peer investors, etc. In many instances, they’re taking data feeds from us and others and vacuuming them into their dealflow management systems to aid in day-to-day operations. That's suggestive of process which again suggests some desire to scale, make things repeatable. But these investors are not the norm. I think Dave and his team have talked about Moneyball in investing, but a quantitative approach to venture capital may be difficult. Some of this difficulty is driven by data limitations, of course, in terms of sheer quantity vs. something like public markets, but a lot of the reasons that data is less used are structural or behavioral in nature. While there are many reasons data is not used by a lot of VCs, a couple of these reasons are detailed below. Pls note that these are generalizations. As mentioned above, we see and know many VCs taking a very progressive attitude towards the use of data, but…