DUSTIN KIRKLAND: Hello, my name's Dustin Kirkland.
I'm a product manager at Google.
And I'm here with Michelle Casbon.
And she is in developer relations,
working on the Kubeflow project.
We're currently on the path to Kubeflow 1.0.
It's coming soon.
What can we expect to see in Kubeflow 1.0?
MICHELLE CASBON: Yeah, so we just rolled our 0.5 release
a few months ago.
And we're preparing for 0.6, which is sort
of the last leg towards 1.0.
And we're really focusing on taking a lot of the learnings
from our initial users, so the ones that
have been with us from the very beginning.
Kubeflow is about a year and a half old at this point.
We launched in KubeCon Austin, a year and a half ago.
And at this point, we're focusing on hardening our APIs.
So we've been listening to our users.
We have a great contributor base,
and they've given us a lot of feedback on what works for them
and what doesn't, which features they like,
which features they don't.
And so now we're hardening those,
we're making sure that people who've installed it
can upgrade in a seamless way, enterprise
features like multi-tenancy, more individualized,
granular security controls, that kind of thing.
So making it a robust experience for enterprise, for
startups, for everyone in between.
DUSTIN KIRKLAND: And as I understand it,
one of the real goals of Kubeflow
is to get these AI/ML tools in the hands of data scientists
without those data scientists also
having to be Kubernetes-certified system
administrators, right?
MICHELLE CASBON: That's right.
We want to provide access for both DevOps and data
scientists, machine learning engineers, everyone in between.
So we don't want to take away any control at the Kubernetes
layer.
That's really important for enterprises.
But we don't want a data scientist
to have to learn about Docker, and containers,
and everything--
all the nuances of the Kubernetes platform.
So one of the first components of Kubeflow was JupyterHub.
And we've really expanded on that
to make it possible for data scientists
to not really ever leave that environment,
to be able to build a model, train it, and deploy it
directly from within an environment
that they're familiar with.
And so the idea is that DevOps has
a lot of fine-grained controls, but they
empower data scientists to do everything
they need to get their work done in a scalable way that
is sustainable, where everyone's happy,
everyone's sort of living in the space
that they're comfortable in, and not stepping
on each other's toes.
DUSTIN KIRKLAND: And in terms of community,
and the community contributions around Kubeflow,
Google fits into that, of course.
Maybe tell us a little bit about what
Google does in the community, and where
else you see community participation in the Kubeflow
project.
MICHELLE CASBON: Yeah, so Kubeflow started out
as a Google project.
A year and a half ago it was launched at KubeCon.
And we've seen an incredible uptick
in the number of contributors, number of PRs that we've seen.
One of my favorite new features was just
debuted a week ago at our community call.
We had a few researchers in Italy,
they showed this demo of some notebook features, so something
that makes it even easier for data scientists
to essentially add tags to a notebook
so that it will automatically run within pipelines.
And this whole story, the more people
use the platform, the more they come up
with ideas that we haven't considered before,
and the more that community base grows.
So we had a really successful happy hour last night.
I think there were only a few Googlers.
But we packed this tiny neighborhood bodega.
There were people spilling out into the streets.
And it was people from all over the world.
And that's really encouraging to see.
We have a lot of big support from Azure.
We have a bunch of AWS integration.
It doesn't just run on the Google Cloud Platform.
It's designed to be a portable platform for running
machine learning anywhere you have a Kubernetes cluster.
It uses vanilla Kubernetes APIs.
And the influx of people coming from all over and adding
to that has been incredibly encouraging.
DUSTIN KIRKLAND: Yeah, I can see it.
And tell me a little bit about the applications
you're starting to see, maybe some the ones
you're most excited about seeing developed on top
of the Kubeflow platform.
MICHELLE CASBON: So the thing that I'm most excited about
is--
part of the keynote was a few people from CERN
who came up and talked about this public data set
that they have.
So they replicated the Higgs boson experiment,
and they did that on GKE.
And so I'm meeting with them very soon.
I get to actually visit CERN and take a tour.
I've been wanting to do this for so many years.
If you've seen the documentary "Particle Fever,"
it is incredible.
And so I get to see that in person.
And I'm hoping that, when I meet with the team,
that I can come up with some sort of way
to get them running on Kubeflow.
Because it would be amazing to work with that data set
and to sort of help them along their journey.
DUSTIN KIRKLAND: Michelle, thank you very much.