Shawn Wang: evaluation

Transcript context

…And that’s, that’s very good segue because... So Airflow is great, but Airflow is more about you, you have something and you wanna repeatedly run it in production on schedule. It’s less about you as a team developing things and being able to share, and you grabbing the standard pipeline and saying, “Hey, I wanna change this tiny little component in the huge sea of data processing, and I don’t wanna-- I wanna run ten experiments on this, and I wanna do hyperparameter optimization.” All that is very hard to do with Airflow. It’s very easy to do with Tango. Tango is m- more about, it’s everything about group of people Running experiments, it might be agents too nowadays. Uh, running experiments cheaply, collaborating, sharing results. Uh, you don’t need to understand fully. You, you grab-- you clone somebody else’s experiment or somebody else’s pipeline, uh, run, uh, change small piece, run it, be, like, get it to production state, and then ship in one click. So then the... You don’t have to port it into any other system to, to run in production. You can just run the same experiment. It’s, it’s fully production ready. And, and it’s, uh, it has lots of... Again, as I said, it’s third generation system. The original one was, I would claim there was Ether and then, uh, at least in my career, Ether was the first, first, uh, that pioneered this type of approach. And then there was, uh, Nirvana, which, uh, uh, at Yandex, which did kind of sec-second take on this. And now this one aggregates the, the learnings from all of those and, and Airflow as well to, to get to the state where you try it, it, it feels kind of magical. Uh, ‘cause now everything is based on content, uh, hashes. So even if the version changed, but if the output didn’t change, nothing is being rerun. It’s very efficient. If you... Multiple people start experiment that needs the same sort of data preprocessing, it’s not repeated multiple times. It’s automatically done only once. If you start ten experiments that all require, you know, some, some data preparation first as the first step, and you don’t have to coordinate for that. Like, you don’t have to know that other people are starting it. You now, it’s very easy compos-, uh, composability, any language you can u- uh, you wanna use, and it’s very visual. So you can see immediately, you can edit it easily, you can assemble small things with just even mouse clicks if you want to, and, uh, share, clone. And everybody knows also it’s fully kind of static in the sense that we rerun it second time, it will exactly have the same results. Like, you will never have to do digital archeology. So full versioning and everything is also there. Uh, so, so people can, uh... It’s open source. Go to the GitHub repo and, and, uh, check it out. Uh, and it is also a really good, uh, blog post about it. I think all these is, like, really appealing. The, the, the, the thing that I think sells me the most about it is that, um, sort of development to production transition, right? Which I think, um, a lot of people haven’t really solved that, uh, strictly, right? Like, we develop really, really well in, in Python notebooks, but then, you know, that’s obviously not a sort of production ready process. I think that, like, any way in which that is solved, I think is, is very appealing. Then the other thing that you mentioned, which also raised my eyebrows, was content-based caching, which you mentioned is, is, um, you know, is ve-very much, uh, um, a sort of efficiency measure about, uh, you know, just like recalculation only on, on sort of content addressing Which I think makes sense. Uh, it surprised me that the savings could be this much, but maybe I just haven’t worked at your scale where there’s so much duplication, uh, that people just rerun because they change a single ID upstream. It does, yeah. But it’s not only you rerun. The, the main savings are coming from the fact that you ran it, you got your job done, and you moved on. Then- Yeah ... somebody else in some department you don’t know existed runs the same task, but on a newer version.…

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