- Thanks Clemens.
I'd like to welcome Stephan Schwarz
and Sebastian Findeisen from Daimler.
It's great to have you here
to share your knowledge and experience at Summit.
- Yeah, thank you Dina.
Yeah, thank you for inviting us.
It's really a great pleasure for being here.
- Yes, it's great to be here.
- Stephan, there's been a lot of discussion this morning
about the evolution of Data and ML,
and how Data Teams, their processes and their tools
are adapting over time.
How's your data journey changing at Daimler?
- Well, I think like many other companies,
we're also somewhere on our journey.
Our journey didn't just start recently,
but it changed tremendously.
On a cultural perspective, it's like from a special group
of people that make use of data
to rebuilding the bridge and having engineers
and data people really on the same page.
So we've being part of the journey
with real production equipment,
with different IT system, and it whole changed,
like how data is used in (indistinct),
it's quite fascinating.
- That's a really good point about the cultural changes,
but tell us a bit more about the technology side.
How have you built this?
And could you share some of the details
around the data platform, X Dollar?
- Yeah, well...
X Dollar is really our core cloud picture,
which is built within the Microsoft Azure Framework.
Um, I think we created our own security mechanisms
like, build your own key and stuff like that
to ensure that always is used in fulfill our compliance
and security needs inside Daimler.
Um, one key success factor we have,
is that we're working very closely
with our central IT colleagues from the center of excellence
that are the internal authority inside Daimler,
regarding X Tolo.
And for example,
they are building architectural group prints
that could be used all over the company.
So, today we use it as a big data platform,
mainly for analysis and forecasting
with large amount of data.
So it means like X Tolo, cleared our way
with bringing our own data into the cloud.
- So Stephan, we've heard a lot about autonomous
and assistant cars,
and customer applications like Mercedes me.
But one area we don't often think about is ML
in the production process.
I've seen your factory videos and it's actually very cool.
Could you tell me a bit more about the role
data plays in production?
- Yeah, well...
Data always played an important role in productions.
Like for a long time, we have grading measures
based on our daily or longterm KPIs
or expert to make use of data,
to understand and fix root causes, downtime, errors,
and so on.
But, through role of data
and awareness of the value of data,
I'm feeling greasing tremendously.
Like regarding production, we could ask why so?
But you know, car production
and especially talking about producing
high level luxury cars,
is not just one single complex process.
It's a real bunch of highly complex,
real different processes.
And looking at Mercedes,
we always had a very high expertise
and really brilliant experts in our production processes.
But coming to data,
even the best human expertise comes to it's limit
when you're talking about really rare events,
high dimensional influences,
and so we learned that optimizing in our processes
and improving our KPIs,
we really come to limits.
And with data and the new technologies that come with it,
we are now able to read better reports to these experts
and give totally new insights.
- So Sebastian, could you give us an example?
- Yeah, sure.
Let us focus on one out of 20 different joining technologies
we have inside our body shop,
which is called Start Welding.
Start Welding is basically a specific method
of those welding technologies,
where we track numbers of different parameters
that describe basically the process itself.
So giving you an example,
we track the material, the environment condition,
and then of course,
process relevant information like the voltage,
the current and the position of those robots.
So, collecting all this information
and bring it together is one of the key facts.
And to give you an example,
our production or our welding process
takes roundabout 200 to 400 milliseconds.
And therefore,
we need a rate of one millisecond to collect it.
And there you see the complexity of the process itself.
And there is where machine learning
then helps us to understand
and reduce the complexity of the process,
which has basically set 60 different
or influencing dimensions.
And basically, we can with machine learning,
identify the anomaly encore
and identify the root cause of this anomaly.
And today welding expert can use this information,
which is basically visualize,
to find and adjust and improve the parameter settings
of those machines.
Um, so yeah,
basically the machine learning technology inside
Start Welding helps us to really reduce the amount of work
and definitely increase the efficiency
of the process itself dramatically.
- So, that's really interesting.
So, how does that scale from an ML Ops perspective?
- Yeah, that's a really good question.
Because in the past we started our projects
always from scratch,
so we started from new
and when we made our models production ready,
we had to do a lot of manual work
in order to keep the models up and running.
So nowadays, we use a lot of key components inside Azure,
our X Tolo and Databricks.
One of the key components is basically
the Delta Lake itself,
which is the underlying infrastructure for saving our data.
And then, we use numbers of different components
like the job scheduler,
all of the Databricks environment,
where which helps basically to run our strips
in an automized manner,
together with the Azure data factory.
Not a key element, especially for the ML world is ML flow.
ML flow helps to keep us track of our models
and automize the evaluation
and the monitoring of our (indistinct), basically.
So, coming back to the example of Start Welding,
we're using a set of 1,400 features,
basically, which are created out of the
60 different parameters we originally track
and ML flow has done the outstanding opportunity
to gather with the feature store,
to track the performance of each algorithm we deploy
and also to automize the deployment as itself.
So, today we are in a partnership with Databricks,
where we can influence Databricks' products
in an early stage.
And this helps us to reduce our complexity
and especially reduce the deployment time dramatically
inside our production.
- Stephan and Sebastian,
it's been great hearing your story today.
Thank you so much for joining us.
- Thanks for inviting us.
It's great to be here and it was really a pleasure for us.
- Yeah, thanks again.
And it was really amazing being here.
- Thank you, and over to you Ali.