The Changelog – Episode #380

Productionising real-world ML data pipelines

with Yetunde Dada from QuantumBlack

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Yetunde Dada from QuantumBlack joins Jerod for a deep dive on Kedro, a workflow tool that helps structure reproducible, scaleable, deployable, robust, and versioned data pipelines. They discuss what Kedro’s all about and how it’s “changing the landscape of data pipelines in Python”, the ins/outs of open sourcing Kedro, and how they found early success by sweating the details. Finally, Jerod asks Yetunde about her passion project: a virtual reality film which debuted at the Sundance Film Festival in January.

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Yetunde, we’re here to talk about Kedro, which is a Python library dealing with data pipelines. First of all, thanks so much for joining us on the show.

Thank you so much for having me.

And we should give a thanks to Waylan Walker, who requested this episode just recently, actually. Sometimes our requests take months and maybe even years for us to put the show together. This one was relatively recently. I should mention to our listeners that we do take requests at Changelog.com/request. If you have a topic, or a guest, or anything you’d like to hear on the show, just head over there and let it be known. We’re happy to make shows that you all want to listen to.

We have Yetunde here, who is the product manager at QuantumBlack, and is on this Kedro project. Tell us about QuantumBlack and what y’all are up to.

Sure. QuantumBlack is an advanced analytics company that was acquired by McKinsey a few years ago. We’re in the consulting gig, and what we basically think of ourselves of is we’re the Black Ops teams that go out to different companies around the world, and across many different industries, and what we do is effectively deliver functional machine learning code in production. So our success model looks at not only do we solve very difficult use cases, that have very challenging design problems, but we also have clients that are able to maintain their own codebases when we go. So that’s what we do.

And Kedro is your first open source product, I read that’s the case… Tell me about the experience, why is it open source, and all that stuff.

It’s actually very interesting that you’ve asked that… Kedro, yes, is the first open source product that we’ve ever had coming out of McKinsey and QuantumBlack. It was quite an experience open-sourcing it, because it’s very difficult to get corporate open source rolling, especially in a place where it hasn’t happened before. We open-sourced Kedro purely for client need. So what would happen effectively was that our data science teams would go out and use Kedro on their engagements wherever they were going for their consulting work… And they would obviously work with the client’s data scientists and data engineers, and find that everyone really enjoys using Kedro.

[00:03:56.13] They’d suddenly have questions around “What do we do to keep on using the tool after we leave?” And the answer to us became “Let’s actually open-source this tool, so that our clients have access to it”, and we’ll be able to access upgrades, we’ll be able to access an open source community so that they can help further engage with them as they use the tool. So that became the primary reason for us open-sourcing.

We’re very excited about open source in QuantumBlack. We’ve just recently open-sourced another tool called CausalNex. CausalNex is a causality data science library which helps somewhat tackle that problem between causality and correlation when you’re busy assessing different datasets. We think of it as kind of like a way to really give back to the community, and really make a stake in some of the thought leadership pieces that we maintain at QuantumBlack. We have a very exciting data science R&D function that is quite active with trying to solve issues around explainability, around fairness, around live model performance tracking… And really being able to share snippets of that knowledge I think is very exciting for us.

That is very exciting. I should mention that Waylan, when he requested the show, he did say that Kedro is changing the landscape of data pipelines in Python. I’m curious if you agree with that, and then secondly – I mean, it seems like to me if it wasn’t open source, that could not be the case; maybe it could be anyways… But I wanted your thoughts on the open-sourcing of Kedro and how that has helped it to perhaps change this landscape, as he says.

I am so excited that Waylan finds that Kedro is changing the landscape for data science… Maybe I should actually describe what Kedro does.

Please do.

We think of Kedro as your way to apply software engineering best practice to your data science and data engineering code. It follows this whole principal that if we get everyone operating at a higher standard for software engineering best practice, we make code that is reproducible, it’s deployable, it’s versioned, and it makes it easier to put that code into production when it’s time to go live on models.

It kind of fits into this whole problem space where for the longest time data scientists have not necessarily – no, they’ve used code to solve the problem, as opposed to seeing code as the end goal, which is what is required for that code to be functional in the machine learning practice, and to have value for business. So Kedro essentially says “You have your standard way of working. We’re just gonna make some slight modifications to it, so that you have more robust code. In the end you get this production-ready code.”

So I’m really excited that he says that we’re changing the base of how data science should be done, because we believe in getting to the heart of users and really just having empathy for your workflow, and being able to enforce software engineering best practice is easy using Kedro.

Other exciting things that are happening because teams are using Kedro, from what we’ve seen from the open source community, is that people are following the trend of creating usable analytics code as well… Because there’s a certain workflow consistency that happens when you have well-structured code, and they’re now able to build their own reusable analytics libraries on top of Kedro, which has been quite exciting to see.

I was digging through Kedro docs as I was trying to figure out exactly what is this thing that I’m looking at, which is a fun endeavor for many open source projects… It’s like, “Okay, I see what’s written on the label… What exactly is going on here?” First of all, the documentation is really good, which is indicative of the Python community; I’m not sure about the data science community, but you all are killing it there… And the Hello World is a very nice example of knowing exactly what Kedro starts you off with.

As I was reading it, it was more akin to me as a software developer as like a conventional framework; a thing that establishes norms, gives you buckets, very much kind of convention over configuration concept of “Here’s where this goes, here’s where you put your pipelines, put your data here etc. etc”, and if we all follow these norms, life is better, life is smoother, it’s more production-ready. Is that kind of the idea?

[00:08:05.25] That’s actually completely the idea that it’s based on. You’ll note that – I don’t know if you’re familiar with a tool called Cookie Cutter.

Cookie Cutter follows this whole methodology - it’s another open source project - that if we work in a standard way, so if we have the same setup in terms of directories and where we place certain files, it guarantees workflow consistency that makes it easier for you to work with yourself in the future if you have to look back at your code, and also easier for other people to work with you… Because in a sense, your code becomes self-documenting, because you where things are stored.

There’s a derivative of Cookie Cutter called Cookie Cutter Data Science, and Kedro is actually built on top of Cookie Cutter Data Science, but takes into account how teams also need to construct data and ML pipelines, as well as also thinking about things like “How do we do data abstraction when trying to load data?”, because there’s many different ways of loading data using different Python libraries. And also things like “How do we do versioning of these datasets of models themselves so that we can actually reproduce runs?” Because the complexity of reproducing code - if you’re building a software application and reproducing a previous version of the code, it might just be having a look at the logs to see what happened when a failure happened, and then having a look at the code that created that, and any other inputs that you have. In machine learning, that becomes a bit more complex because of the different variables that you would have had, that would have made that machine learning pipeline run, for instance… And Kedro kind of tries to – I did say it’s basically software engineering best practice, so it does try to implement those good sense principles in the data science world.

That’s awesome, and definitely something that is needed in the data science world… As has been reported to me. I’m not living there, so I’m just going based on what you’re saying and what people on Practical AI have also said - our other show - about machine learning.

Now, I’m coming from the other direction; I have a software background, and less of a machine learning background… And I’m not gonna speak for our audience, but I’m sure there’s plenty of them out there who are in a similar place as me. So when I look at Kedro, I see the conventions, I see the best practices in place, and I’m like “Yes, this makes total sense.” But then I get to the other bits that I’m less familiar with, and I start to wonder “What are these things and how do they fit together for me to implement something in production and make it useful?” So when we start talking about data pipelines, nodes etc. can you explain some of the jargon perhaps and some of the things involved in starting a Kedro project, and maybe taking one into a production application?

Okay, so getting started with Kedro means after you’ve PIP-installed the library, simply running a CLI command for Kedro New will have you creating your project template. It’s actually where you find the Hello World example is actually based, so you can get started in less than 30 seconds with your first Kedro project.

You can execute another command after you’ve created your project template, Kedro Run. You would have made your first Kedro pipeline run. So when I break down the components that are needed to actually understand Kedro, there are five. You’ll understand that we have the project template, which is basically our series of files and folders that are generated by the Cookie Cutter Data Science template. It’s kind of like best practice for “Where do I store bits and parts of my code?”

Although there are quite a few directories that will focus on even where you put your Jupyter Notebooks, to where you store your results, the two most important folders in that directory are where you put your configuration, which basically looks at - if you wanted in simply-explained terms - how do we remove hardcoded variables from our machine learning code? And there are two types you typically find - file paths and parameters. So how do we remove that from our machine learning code, so that it’s more reproducible? So you’ll put your configuration in a specific place.

[00:12:02.21] We also talk about our data catalog, which is one of the library components of Kedro… Which is basically a series of extendable data connectors. If you extend our abstract dataset class, you can actually customize and create your own datasets for things that we don’t typically support. But we support most file formats, so .csv all the way to Hadoop files, and [unintelligible 00:12:22.29] tables as well.

Then we talk about the Source folder being the next most important directory for you. In the Source folder you’ll actually find the concept of nodes and the pipeline. Your nodes are just pure Python or PySpark functions that accept an input, and have an output. You use these nodes to actually construct a pipeline, because it’s basically a series of nodes which have their inputs and outputs mapped to each other. So the pipeline can actually essentially work out where your dependencies are when you’re building the pipeline, and that is the basis essentially of a Kedro pipeline.

Those are the primary concepts. There are obviously additional features built on top of all of these features, including the data catalog allowing you to version your machine learning models, and your datasets. And there’s all sorts of ways that you can extend Kedro however you feel.

Okay, I think I’m getting it. So you have your data catalog, which is your input data, whether it be CSV, or JSON, or SQLite, right?

The data catalog manages both loading and the saving of your data, so if you specify where the output should be saved, then Kedro will handle that, too.

Oh, okay. So it’s on both ends. In the middle then you have your pipeline, which is stitching together these nodes, is that correct? The nodes are pure functions, so maybe it trains the model, maybe it does something else, predicts something… And then you have your pipeline, which is basically saying “Call function A, then B, then C, then D…”, or whatever. Is there more to a pipeline than just like “Here’s the order of operations?”

No, that’s basically it. There are all sorts of things that you can do with the pipelines, there are additional features that you can do with it, but you’ve basically nailed down why Kedro is so easy to learn.

It’s basically specifying “Which function do I need? What is its input? What is its output?” and then “Let me put them all in this pipeline format.” The pipeline syntax is very easy.

Very cool. So then the last bit was the output then. You said this goes back to the data catalog, but what is the results of these pipelines producing? Is it on a case-by-case basis, based on what you’re trying to gather from your model, or is it always the same kind of thing that comes out on the other end? What do our results look like?

It depends on what you’re using Kedro for. An output could be a machine learning model, so even just a pickle file; what you would then use to test future predictions with… Or it could be maybe a table that you need to be loaded into some sort of database.

So it depends on what you actually need the output to be, because that’s what you would set up your pipeline to do.

I think I’m getting it now. So if we go back to the Hello World - because it’s a simple one for us all to wrap our heads around - it’s the Iris dataset, which is a well-known dataset of different pictures of flowers, these three different types of iris plants… And the goal is to classify. So you give it an image, and it says what kind of an iris it is, or maybe you just give it some measurements of the petals… Talk us through the Hello World, we can use that as a reference point for conversation.

Cool. So the way that this pipeline is set up, it has four notes. One which will actually take in an Iris dataset, so the actual values, and it will split the data into training test samples, and also do some sort of data preprocessing as well to clean it up, so it’s in a format that can be used.

[00:16:05.17] Then the next three nodes will train a model, then create the prediction model for you. And then how this pipeline ends - it ends slightly differently. This is why it always links back to what problem were you trying to solve, because it allows you to report accuracy… So you eventually in the last node can feed in a value, and then report accuracy on which – based on which values, which iris flower are we looking at.

I see. So like you said, it’s all based on what you’re trying to accomplish. In this case, that’s what it’s trying to output, so… There you have it; the accuracy is important.

Yes. And that’s why it will actually just tell you what the accuracy is at the end of the pipeline. So if you did a - like I mentioned, Kedro New, and then Kedro Run, once you’ve changed into the project directory, that’s created for you; then it’ll actually just tell you what the accuracy of this model is.

Break

[00:17:05.17]

So your first open source project at QuantumBlack, and so far a success, perhaps changing the landscape of data pipelines - I like that line. And with any open source project, there’s always big wins and there’s often big fails, struggles along the way… You mentioned the reasoning behind it was your clients needed some sort of a sustainability plan for these tools that you were working on for them or for their models to continue on after a contract was over… What have been some of the struggles or the things you had to consider as you took Kedro open source? Any insights you can share with the community?

One of the most surprising ones was actually our name. We were known by something else internally, and when we were like “Well, we wanna go open source. We’re gonna go public” and everyone was on board, we kind of heard from legal that “Hey, we actually need to check out your name to check that it’s not infringing on anyone’s trademarks.”

I think this is a bit unusual for open source projects where it’s just like a personal project. You would never think that “I need to check my name for trademark infringement”, and it needs to be kind of unique… Kedro is a bit of an abstract name for what the tool does, but we stuck by it and it works. But I’ve actually come to discover that a lot of corporate open source projects, including some friends at Uber, have spoken about doing the same thing… So I was like “Well, at least it’s not unique to us that we have to undertake this.”

[00:20:05.22] Another challenge that we had was really thinking about how do we support our users when they’re using Kedro? Because our initial request for a public slug or a public Gitter was initially paused, and they told us we need to do some risk assessment on those platforms to work out for instance how do we enforce a code of conduct for users behavior on the platforms as well, so that we have free and fair communications on them.

So we knew that beyond GitHub issues and Stack Overflow, both of those channels that we do watch, we needed to do something else. You actually see that we spent a lot of time on our documentation, so I’m really glad that you’re enjoying our documentation… Because we knew that when we have users coming to Kedro, they wanna get started very quickly, and if they run into issues, they need to be able to go to the docs and be able to troubleshoot their way through, at the very least. If they’re not gonna talk to us on GitHub issues, which is sometimes a big thing for people to post questions there, or even Stack Overflow… But you’ll be excited to know that we will be getting a public communication channel soon. So that’s in the works for us to eventually release to users.

And then the third thing was that we didn’t expect the community to pick up this quickly. That was something that we were not prepared for on the team. Kedro is maintained by nine people. So it’s me as product manager, we’ve got Ivan Danov as the Kedro technical lead, and then an amazing group of software engineers, machine learning engineers, visual designers, and a front-end engineer who maintains the data pipeline visualization tool called kedro-viz… And we were not ready for how many GitHub issues were created, pull requests were created, because people wanted to contribute code back to Kedro and make it better with us… And how many questions we’d get on Stack Overflow.

So that was a bit overwhelming, and I think we’re still finding different strategies to manage it. One that we do do on the team, because we’re inevitably the ones that know the most about Kedro and how to fix different issues, is that we have a rotating role on the team called the Wizard; basically, we have a Wizard, and then the rest of the group are the warriors.

If you’re the Wizard for the week, your job is to basically field all user queries, both in our internal channels, as well as our external channels as well, to try and make sure that users get quick and speedy responses to their questions, or to any issues or feature requests that they’ve raised.

So that was one of the strategies that have made things a bit better… But we are looking at ways to even scale support for our open source users in the future. So we’re looking into new roles that McKinsey and QuantumBlack have never hired before - because we’ve never open-sourced anything - but developer advocates, or dev rel… I think it’s sometimes called developer relations - to come onto the team and help us really scale out what that model will look like for us. So yeah, those are some of the unexpected – three of the unexpected things that we didn’t realize when we were open-sourcing would happen.

Awesome. Very good. Let’s go back to number one, let’s go back to the name. What does it mean, Kedro?

It means “the center” in Greek. We kind of look at Kedro as being the center of your data pipeline, because of the way that it forms infrastructure/foundation of your analytics project, or ETL pipeline, or whatever you need to build. So Kedro means the center.

The center. And so after you came up with the name, that’s when the – was it a trademark search went out? I’m sure there was other searches as well; you searched on GitHub, and Twitter, and domains… Were these also things that you took into account before picking the name?

Yes. It was actually a lot more involved. Naming in QuantumBlack has been quite exciting, because Kedro is one of three main products, and we’ll be expanding the range of products that we have.

[00:24:02.05] So naming is always an issue in Labs, because [unintelligible 00:24:04.26] which does all the product engineering… And yeah, the naming exercise was a stakeholder management exercise, because we had to have happy branding, because that was helping with the product marketing and our global head of marketing, Katherine Shenton. But we also had to make sure that the team was also happy to represent the name as well.

So beyond having agreed consistency on a few names, they also had to go through the social media check, they also had to go through reference meaning checks, or even checks in other languages as well, because QuantumBlack is a global organization. And then they had to go through the legal check, to check wherever there were trademarks for this name, in the many jurisdictions that McKinsey operates in, before we came to Kedro. So Kedro was marked as the least risky name.

What were some of the riskier names? Can you share them, or are they just on the cutting room floor, never to be mentioned again?

Some of the more interesting names were – I think when there was no agreement on names. It consisted of names like Barano, [unintelligible 00:25:05.28] which is a plumbing company… And the list of five names that included Kedro in the end, that went for final legal verification, included Braze, which kind of worked, because it referred to welding, or stitching together pieces, which is what the pipeline does. We had Knittic as well, which is kind of also playing with that whole thing of knitting things together.

Knitting…

Yeah, Knittic. You see, it doesn’t work when you try and say it, so that was one of the reasons why this name failed. And then Spindle, because we’re trying to reference that whole thing of many threads joining together,

I like Spindle.

Yeah. But all those names failed the little verification…

[laughs]

And we got Kedro in the end.

There’s no worse feeling than when you come up with the perfect name, and then you go do all your due diligence, and somebody else is using it. You’re like “No…!”

Yeah. The checks also included GitHub repositories as well, because we knew we’d have to survive in that sense… So it was really an adventure to find a name, and I’m really glad that we settled on Kedro. It was the one that fit

Some good things about the name Kedro… I think I even wrote this up at one point [unintelligible 00:26:14.02] the anatomy of a good name, and I’ll say, two syllables - great; the fact that you can spell it easily after hearing the word - great. It’s not ambiguous in that way… But it is intriguing, because when you hear it, you don’t know what it means. There’s no immediate attachment - at least in my mind; maybe to native Greek speakers there are… But to an American mind there’s no immediate attachment to anything, so it kind of stands on its own.

That’s true, that’s true. Well, we never thought of it; thank you, I will give those reasons to the team.

[laughs] You’ve got the seal of approval… Let’s move on to the point two that you’ve mentioned, the community side and the documentation, and how to figure out where the community meets, the code of conduct, all of these different things. I’m curious, because you seem to have checked all the boxes; we look at a lot of open source project, and I looked at Kedro and thought “Okay… Apache license. They’ve got a license, they’ve got a code of conduct, they have a documentation…” As I said, I’ve been impressed with the documentation… So it seems like there was at least knowledge of “We need to do this correctly” and then also “Here is our effort at correctly.” So I’m curious, was there research gone into how to open-source a project, or had people on the team done it before? How did you guys know what boxes to check and which things you really needed to address before you could open-source it?

So the motivation for great documentation actually came from our technical lead. For a while he’d been passionate about the idea of creating an end-to-end tutorial… So you’ll see chapter three in the documentation takes you through this amazing space flight tutorial. It takes about two hours to run through everything and get you acquainted, from beginner all the way to intermediate functionality in Kedro.

[00:27:57.14] I think it was Ivan’s passion for users and them being able to learn and understand the tool, because Kedro documentation before that had literally just been kind of like the API docs, and the user guide, where we describe how the individual parts of the library - like the pipeline, the nodes and the catalog - work. So really kudos to Ivan thinking and pioneering that we need to do better in terms of how we explain these things, and also using this as a solution for the fact that we can host a Slack channel when we open-sourced.

But in terms of how we set up the code of conduct, and how did we think about best practice with how we set up a GitHub repository, I used to spend weekends going through kind of like people talking about how to do open source community management and what does best practice look like running a community. So it was important for me that code of conduct went in, so that we’d have a way to enforce if something were to go wrong on the repository. Luckily, nothing has… But we’d have a way to communicate with the users by referring them back to the code of conduct, one, and then two, also being able to take action to resolve things.

So yeah, it was just kind of like “How do we have an empathetic view to how someone will perceive a product, and how do they get on the best side of that, and try to put yourself in the users or the first viewers’ shoes?”

On this note, additionally, I do know that open source does have diversity issues as well, so I’m really excited to eventually see hopefully PRs from women, or women-identifying people on Kedro.

So yeah, at all times we do also have a style guide for how we communicate with our users, as well. That was also something that we set up. So how do we say “Thank you for contributions”, how do we respond to questions as well - and that is a team standard. Because we never want to create an environment where someone is offended and doesn’t wanna come back and interact with us on our different channels because of maybe a comment, or whatever the case might be. So those were things that we did look at.

Very cool. So I will say to your third point, which was you were a bit surprised by the level of success or people glomming on and using the project, I would say it probably has to do with the stuff we just talked about - the thoughtfulness and the TLC that you put in around open source, and doing things well. That being said, I’m curious if you had some sort of a launch plan, or were there ways that you said “Okay, Kedro is out there. We want people to use this, we want people to try it”, were there press releases, was there blog posts, or was this just a thing that grew organically after open-sourcing it?

That is a fantastic question, and there was a huge release. It was McKinsey’s first open source product, so everyone was very excited. The other thing that we realized in hindsight is that – I’m really passionate about product marketing because you can kind of solve it as a… It’s a user problem. It’s “How do I make sure that you’re getting the right information, so that I don’t waste your time?” And you also learn what value something might have for you, because you consumed it in a short time and in a form that you needed it in.

So I kind of approach product marketing just like I approach product management, and it’s “How do we optimize for the time that you have?” I was able to construct a massive marketing campaign with the Kedro team and with our head marketing and with McKinsey branding in order to actually deliver what was our massive open source release in June of last year.

So price releases went out, articles released towards data science, there was social media, there were some webinars done… And yeah, we just wilded out a little bit on being able to (I think) basically make history for the firm, and release their first open source tool, in a place where McKinsey is recognized as having this amazing intellectual property that we never open-source. So it was really just exciting for us, so that’s why we went big on that launch.

[00:32:00.09] Do you think the success of Kedro will lead to McKinsey open-sourcing more things, or this more of a one-off because of the client need?

Good question, and I can actually show you an example where that’s not actually the case, because… It must have been now – we’re going on to two weeks ago, we released our second open-source project, CausalNex, which is essentially a Python library that helps data scientists address the question of causality versus correlation in your datasets… And we released this one purely because it was an exercise of how do we showcase some of the R&D work that we have done on client projects, and eventually have made their way into more formal products, because we have been able to try and test those methods.

The really cool thing about CausalNex is that the research that it’s built on was actually presented at NeurIPS; not at the last NeurIPS, but the NeurIPS before, and we had quite a few data scientists that were intrigued about this whole NO TEARS approach for assessing causality. This year we’re finally releasing the library that we were able to build using that theory.

So yeah, we’re still working out what does our open source strategy look like for the firm. There are still so many interesting questions about “How do we tackle it? What do we decide to open-source? What is our process?” Finding out that, because I still think that there are places that we could optimize the process of it better… But this is an exciting place that we find ourselves in. We hope that it inspires many more people within the firm to consider open-sourcing.

I think I do get emails with people asking like “How did you open-source Kedro?” in hopes that they can do the same. So maybe it was just the first of many, I really do hope.

For those curious about CausalNex, I have scooped it up and we’ll include a link in our show notes, so you can click there and check it out.

Break

[00:34:01.12]

Switching gears a bit, let’s talk about fun stuff. You are involved with something that looks very cool, the development of what you call Social Impact Virtual Reality Film, as a Sundance New Frontier Lab fellow. Tell me about that… It sounds intriguing.

It sounds like you’ve been digging around on the internet, which is cool… [laughter]

Maybe just a little bit…

[laughs] So I was a Sundance New Frontier Lab fellow along with a co-creator, one of my best friends, Shariffa Ali. We’ve been best friends since like high school [unintelligible 00:37:20.05] in 2018. And the reason we were Sundance New Frontier Lab fellows was because we’d been working on - at that point it had been two years - a virtual reality film called Atomu. So Atomu is based on this Kenyan myth that if you walk around a sacred tree seven times, you change gender. So you go from male to female. And it’s kind of like this interesting comment on gender fluidity ideals, which are kind of seen as unafrican.

Obviously, it now tackles this whole thing of why is modern day Kenya so averse to gender fluidity ideals, and homosexuality as well… So there are a lot of issues that we can now dig up; obviously, you look at religion and colonialism as factors that would impact that.

Moving on from there, we were excited (also two weeks ago) to actually head to Sundance 2020 and actually premiere Atomu. We’ve been iterating on the piece since then, and we were able to actually showcase the piece at Sundance. That was a really cool experience, and there’s still more work to do, because that was essentially – Atomu is in its fourth iteration right now, and there still will be a fifth, which really focuses on how do we distribute the film to different partners in the U.S, Europe, and also back home for us; Shariffa is from Kenya, from South Africa, so how do we do that in an accessible way.

So this was Sundance Film Festival 2020, it just happened a couple weeks ago… So a fresh thing that just happened. I’m curious how you even film a virtual reality film. And you were talking about iterating - are you filming more things, or…? Tell me about the process.

Sure. The way that Atomu is set up - it’s built in Unity. It’s also a dance piece, so we had access to two dancers who would essentially do motion capture to actually capture how they’re moving. You can build an avatar from that and place it in any environment. In our case, we placed it two in the [unintelligible 00:39:30.18] which is the sacred tree, are typically placed. That’s essentially how the environment was built.

There was also some thought around how do we do different user experiences as well, because we did optimize the piece for the Oculus Quest. We specifically waited for the Oculus Quest to come out, because we knew it was a higher resolution virtual reality headset… So higher than the Go, for instance, but still more accessible in terms of price than the Rift was, the Oculus Rift. So we specifically designed it for that.

[00:40:05.16] And we also were fascinated about the multiplayer experience, which we’ve had some moderate success with. I think the next iteration will fully nail down what the multi-user experience is supposed to look like. But that’s essentially how you do it. So you can decide – I think there’s many different ways of filming virtual reality experiences. I think you have seen the 360 video documentary pieces which were still released… But a lot of pieces will focus on using motion capture to build avatars, and then eventually constructing environments around the different avatars that you’ll have.

So in terms of presenting that at a film festival, is it just a room with a bunch of these Oculus Quests, and you go in and have – because it seems like an individual experience, versus a shared experience.

That’s a good question. How our piece was constructed is basically that. When we were presenting at the Sundance New Frontier space within this year’s festival, we were in the biodigital theater, which was a specific area set aside for what they call multi-user virtual reality experiences. In Atomu we were able to get seven users in at the same time. Seven that could each put on your headset, each have your own set of controllers, and you can each affect the piece based on how you’re interacting with it.

In the piece you are ancestors who are basically saying that this way of life is right, and you’re following a character called Wacici as they make not a gender-based transformation around the tree, but what we call the journey to be the most honest version of themselves. So you help this character along the journey as they kind of like dance and struggle around the tree, in their journey.

It was important to us to have the multi-user experience. It was a design point for us, because the artistic intention for it is everyone understand what it’s like to not be your true self, everywhere. It’s just a human experience of sometimes being unsure of yourselves because someone’s made a comment, or doubting yourself, or even hating parts of yourself. And you being able to support someone on that journey means we would love for you to be able to know that you can support other people in their lives on their pursuit to be the most honest versions of themselves… And you also recognize that you’re not alone in that experience as well, because there are other people going through the same thing.

So that was why we opted for the multi-user experience. We still have some technical challenges with it… The system that we’re using currently is quite expensive, which goes against one of our design principles of making this piece accessible. But we’ll be looking at different ways to actually try and code it – kind of like hack the Oculus Quest, if they don’t help us in the end, to do the multi-user experience without having additional gear… Because that is really, really important for us in terms of distribution.

Wow. So how was it received?

Very well received. Shariffa and I also made certain choices to whether or not we went explicit on the way that we describe some things, versus very abstract… And we kind of like strayed towards the still kind of like abstract-focused, but people got the intent. People understood why they were in the piece, why they were the ancestors, and why it was a multi-user experience.

They also understood how the piece was constructed in terms of Wacici’s journey and understood everything that happened there… So we have received really fantastic feedback. We have obviously opened it up and told people that “Hey, we’re still iterating on this piece, so any critical feedback that you have, do let us know, so that we can build it into the piece.” So yeah, it’s been very well received, and we’re very happy. This has been a long-time project, but yeah, I’m really excited for 2020 and finally completing Atomu and seeing the impact that it was supposed to create.

[00:44:00.06] Yeah, I was just gonna ask if it seems that this is like a living project, or if it’s just one that’s still being formulated and will eventually come to its natural conclusion. It sounds like there will be a completion step, when you brush off your hands and say “We’re finished.”

I don’t know, virtual reality experiences - at least the model that currently exists - means that… I guess because the tech is moving, but it’s not really moving that fast, it means that they live longer lives than I think mainstream films or mainstream media would… Because your HTC Vive, or your really old Oculus Rift can still watch movies from today, for instance. So in terms of overall end of Atomu, I’m not sure that there is, because we are considering different models of distributing the piece in museums, and even schools, as well as working with non-profits as well…

Because the intent of the piece obviously is the journey to be the most honest version of yourself, but we can bring someone to understand that it’s actually about how do we deal with LGBTQ issues and accepting people that have different sexuality preferences. So really being able to use it with non-profits is also important for us, too.

So we hope that the piece lives on because the intent and the meaning behind why it was made still remains relevant. My ideal world, I think - not that I know Atomu could achieve this; it would be wild if it could - would be that the piece actually wouldn’t need to exist, because everyone was just comfortable in their skin and was accepted. But we have to do our part, that’s why we hope the piece lives on.

Very cool, Yetunde. How do people get in touch with you out there on the internets? Where can people reach you?

On the internet, I’m quite active on Twitter, so you will find me tweeting away. I’m at @yetudada. I’m on Instagram as well, but I don’t really use that one anymore. If you find me on LinkedIn - I know you, only then will I accept you, so Twitter is probably your best bet. [laughter] Twitter is probably your best bet. Yeah, that’s the easiest place to reach me.

Otherwise, you’ll find me on the Kedro GitHub repository, or on Stack Overflow, especially when I’m the Wizard and I’m answering questions. So yeah, those would be the two channels to find me.

Very good. Well, thank you so much for joining us today. This has been a very interesting dive into Kedro, and also into Atomu and the work you’re doing at the Sundance Film Festival and beyond. Good luck to you on both endeavors. Any final words from you before we say goodbye?

QuantumBlack Labs specifically, so the product engineering part of QuantumBlack - we’re hiring. We need help finding amazing people that can do software engineering. So from full stack engineering all the way to even just being Python-focused devs - we’re looking for you. We’re also looking for product designers as well. If you spike on UX, you will be loved; if you spike on the visual side, you will also be loved. And product managers as well.

We’re really growing out the team. And as mentioned, specifically on Kedro we’re looking for a developer advocate and dev relations people. And if you can do Python as well as being one of those people, then we love you more. So yeah, if you’re interested, I think the best place is to head through to actually the McKinsey website, which would host our job offers… But otherwise you can just ping me on Twitter and send me your CV, and I put it straight through to the recruiters. It makes your life so much easier.

There you go. Hit her up on Twitter and get that ball rolling. Well, thanks, Yetunde. This has been awesome. To everybody else, we’ll talk to you next week.

Changelog

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