- All right.
We are official.
- Okay, so why don't you just start
by telling me your name and title again?
- My name is Dwayne Newsome.
- Jolie Vitale.
- Joe Hagerty.
I am a data scientist.
- Manager of Data Engineering.
- Director of BI and Analytics at Carhartt.
- Carhartt was born out of serving
and protecting railroad workers.
- 131 year old company.
- Since 1889.
- Carhartt customers are hardworking tradespeople,
outdoor enthusiasts, all kinds of people.
- Over the past 10 years,
our US facilities have manufactured 31 million garments,
which is an average of more than 3.1 million
pieces of product per year.
- The initial problem was we wanted
to expand our brick and mortar.
- As a merchandiser, it's really important
that we have the right products
when our consumers want it and where they want it.
- It was one kind of key moment
where we thought it would be really good to open a store.
You kind of look at the economic data
and say, "Yeah, that's a growing area.
The population's growing."
And it looked like our ideal place to open a store.
- We got very far down the line of planning
and started to realize that for our wholesale partners
in that market that may not have been the best decision.
- If we did open a store there,
we could cause some problems for retailers X, Y, and Z.
Well that was kind of a big turning point
and then we thought well hey,
you know, we have all this data.
- What?
- We have this data.
- The Carhartt demand system was created
to project demand at the postal code level
by month for all 10 of our distribution channels
in order to make more strategic market selection decisions.
So the data set our data engineers developed
is roughly 1.75 billion records
with a parameter space of 83 million parameters.
- Wow, this model is huge.
- The thing overwhelmed any server
that we could throw at it and basically we gave up.
- This thing needed a high performance computing.
- We really had to scale.
- And that's when we decided to reach out.
- We have a very small and scrappy team.
Microsoft definitely stood out as a partner.
- We really needed people
that could offer the big computing capability.
- Data is allowing us to automate decisions
to start to work smarter.
- Automation is absolutely integral.
We have the Azure Machine Learning pipelines
training, evaluating, and deploying the model.
And then we have the Azure DevOps pipelines
or MLOps pipeline and that is actually doing
all of the rest of the orchestration with infrastructure.
The reason we used the Azure Machine Learning
was not just because of
the MLOps capability that it was bringing,
but also because of our ability to use HPC compute clusters.
- Actually lets us crunch through all of this data
and go from days of compute time to hours.
- What were some of the tools
that were utilized to build this solution?
- Microsoft SQL Server, Azure Blob Storage,
Kubernetes, Azure Data Lake, GitHub,
obviously our Azure Machine Learning platform
which is the heart and soul of this whole thing.
- Working with Microsoft Azure Machine Learning
is allowing us to continue to add
and enhance this model.
We now are using data to project out
so that we can get the right product
where we need it, when we need it.
- We're actually optimizing work at our warehouses.
- We opened three of our retail store locations
in 2019 based on this new way of looking at the market.
Those three stores were already amongst
our highest performing stores
and exceeded their revenue goals by 285%.
We're going to be able to deliver more value,
but we'll only be able to do that with the power
of Azure Machine Learning behind us.