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HASAN KHAN: Welcome to the session
How Wayfair enabled their data science
team to accelerate their ML adoption
and take advantage of the ML ops platform, which consists
of both in-house developed and GCP Vertex AI platform.
I'm your host, Hasan Khan, Principal Architect
at Google Cloud.
With us today, we have Mayank Gandhi and James Emerson
from Wayfair.
MAYANK GANDHI: Thanks, Hasan.
I am Mayank Gandhi.
I head the data and machine learning platforms at Wayfair.
My team is a group of software data engineers and product
managers that are developing platforms and dedicated data
products for our community of data scientists, engineers,
and analysts.
JAMES EMERSON: Hi, I'm James Emerson.
I lead engineering for ML platforms here at Wayfair.
My team is a group of software engineers
responsible for making machine learning
accessible within Wayfair.
HASAN KHAN: Wayfair has been using GCP since 2017.
In 2020, Wayfair decided to look at Vertex AI platform
to integrate with their existing in-house developed platform
and various other valuable GCP services, for example,
BigQuery data, DataProc, et cetera.
This integration requires a modular solution
which has the capability to scale and help Wayfair
to innovate with the velocity of their choice.
Vertex AI is the GCP AI-ML platform,
which allows users to build, deploy, and scale
ML models faster with both pre-trained and custom tooling.
Vertex ML ops tools provide easy management
of data and models with confidence to repeat at scale,
while the pipeline helps users to automate monitors and govern
these ML systems by orchestrating the ML workflow
in a serverless manner.
The AutoML capability helps the user
to develop the high quality custom machine learning models
without training routines.
Vertex AI Workbench is the single environment
for data scientists to complete all of their ML work
from experimentation, to deployment, to managing,
and monitoring models.
It is a Jupyter based fully managed,
scalable enterprise ready compute infrastructure
with security controls and user management capabilities.
Its modular approach protects customer's existing investment
and allows them to fill the gap by adopting
the different Vertex modules.
It's integration with open source frameworks
is also a noticeable feature.
With this, let me hand over to Mayank.
MAYANK GANDHI: Thank you, Hasan.
So before we begin, a quick intro to Wayfair.
We are one of the world's largest online retailers.
We serve millions of customers, and have
over 22 million products.
Wayfair is the leading retailer in the massive home
market, which is vastly different
from other retail categories.
Customers are seeking to bring their vision to life
through the millions of products in our catalog.
The idea of buying furniture through an e-commerce website
was a revolutionary one when Wayfair first started in 2011.
And it's still even in its early stages of adoption.
But this is largely why Wayfair's continual innovation
and growth within this expanding market is so exciting.
Wayfair has built its business selling its products online.
In the world of home goods, this is unique.
Selling online allows us to offer
a wide selection of products and a customized user experience.
Wayfair is not only an e-commerce company,
but it is a technology company at its heart.
Technology powers everything that we
do from our strategically designed consumer
facing websites, to our Wayfair build
proprietary backend systems, which maximize our processes
and functionality.
Our platforms also enable our suppliers and partners
to better serve our customers' needs.
At Wayfair, my team, machine learning platforms,
or ML platforms, serve the awesome and innovative teams
of data scientists and engineers who are developing machine
learning powered experiences for our customers, suppliers,
and other partners.
We leverage machine learning to serve our customers
across their journey with Wayfair, from personalized
recommendations, and to customer service,
with the goal to deliver a world class experience.
Therefore, we are constantly evaluating these experiences
for what is working and what isn't.
Identifying early signals, and being able to iterate faster
gives us an advantage when solving our customer's needs
for building towards their dream home.
For example, on wayfair.com, providing
improved personalized recommendations
that highlight that perfect product that our customers are
looking for early in their shopping journey
is an impactful outcome.
Machine learning is built into the fabric
of our decision-making, allowing us
to learn from our customers about their needs, and thereby,
continuously improving our offerings,
such as our recommendations, our product catalog, and supplier
partnerships.
Together, we deliver holistic and personalized experiences
for our customers.
My team, machine learning platforms,
plays a key role in that.
Wayfair's machine learning platforms
offer a set of integrated solutions, some built in-house,
others leveraging open source solutions,
and Google's Vertex AI platform, together, enabling our data
scientists across the machine learning lifecycle from feature
engineering and building models, training
them to operationalizing them at scale.
When we started on our machine learning journey
a few years ago, we had a small but mighty platforms
team that focused on the highest value drivers for us back then,
which were making feature solving for online inference
and model solving easier.
We built in-house solutions to address these requirements.
But as we grew, so did our needs and scalability demands.
We needed scalable platforms for our everyday purpose
and for peak traffic days such as Way Day.
Therefore, what worked once now required more complex
and ad hoc solutions to just make
it work, for example, feature solving for high data freshness
use cases work well with our in-house
on demand feature solving solution.
But we now have use cases with complex features precomputation
expectations and use cases with much lower latency needs.
At the same time, the effort it took to package and deploy
a model now required many systems
to be integrated with requiring our teams
to jump through many hoops.
This meant data science teams had
to take on more of the engineering burden
to build and maintain such custom solutions.
As you would imagine, this added another layer of complexity
and a level of effort expectation
for our machine learning teams when
developing their solutions.
Further, our data science teams now
required expanded platform offerings
to meet their needs, such as for on demand compute
resources for model training, out-of-box support
for common machine learning frameworks,
and capabilities for easy experiment management
to just to name a few.
So as we began to architect the next generation
of our platform, we did not want to build everything
from scratch.
Instead, we wanted to leverage best-in-class solutions
that were already available.
Having said that, we had solutions
that were performing very well for us,
and we wanted to continue to leverage and improve them.
We wanted to continue to build for areas where we believed
it would create strategic advantage for us
from the intellectual property point of view,
as well as from the data science experience
we wanted to deliver, along with cost and performance
considerations.
Therefore, we needed the ability to leverage
the best-in-class solutions from both open source and vendor
offerings, and then build on these solutions
and integrate them in a modular fashion
with the rest of our ecosystem, together,
offering a curated experience for our data scientists.
Importantly, we needed a partner with whom
we can take this journey with, a partner
who was equally invested in our success.
As we were evaluating vendor solutions,
we partnered with Google early on.
We began our journey with Vertex AI,
and were one of the early adopters of this platform.
Immediately, we were excited about the capabilities
that Vertex AI offered.
In particular, we began with hosted notebooks,
the Workbench product, and model training component.
These solutions are highly configurable
and are scalable to meet our growing needs.
Therefore, it was no surprise when our data science community
adopted these rapidly, and allowed
us to cut our model training times, in many cases,
up to 80%.
Using Vertex AI components enabled
us to offer out-of-box support from multiple frameworks
and the core capabilities that our data scientists needed.
Leveraging these foundational capabilities from Google
allowed my team to focus on the overall platform
experience and its usability in the Wayfair ecosystem.
We will share a bit more on this later in the presentation.
Google has been a great partner working with us every step
of the way, from product support,
to prioritizing gaps and bugs resolution that
were hindering our adoption.
This is a high level and a generalized view
of where we are today.
We leverage not only Vertex AI, but other products
from GCP offerings, such as BigQuery and Cloud Storage.
As I mentioned, we started with Vertix AI
by adopting the Workbench product and the model training
products.
And most recently, we have adopted the Feature Store
offering as well.
While doing so, we have augmented the solutions that
work well for us, such as our in-house developed products,
in particular, our on demand feature
solving solution, our model registry,
and our model solving platform.
Even though we have achieved strong success with Vertix AI,
and have greatly streamlined the end-to-end experience
for our data scientists, we believe
we are still in the early phases of our journey with Vertex AI,
and expect to unlock many other capabilities
in the near future.
My colleague, James, will now walk us
through a demo of an example user experience.
JAMES EMERSON: Thank you, Mayank.
As Mayank mentioned, Vertex AI offers
highly scalable capabilities, including
those needed for training at significant scale.
The immediate availability of hardware
and GPUs without having to handle capacity planning
and reservations for each data science team
made it a great fit for us.
This allows my team to instead focus
on the overall experience for data scientists,
enabling a faster path to production.
Today, I'll be showing you an example
of how we leverage our in-house built products in conjunction
with Vertex AI through an overarching platform experience
to enable our data scientists to perform faster experimentation
and model training.
Adoption of Vertex has enabled our team
to focus on building a great platform experience
for our data scientists.
Mayank mentioned earlier our in-house feature
serving platform, which we have now integrated with Vertex
Feature Store, enabling data scientists
to use a single client to interact with features.
Another example of improving the experience for data scientists
is our experiment library, which I'll be showing you today.
I'll start our demo today in Vertex Workbench, which
many of Wayfair's data scientists
use to perform exploratory data analysis and experimentation.
Wayfair's machine learning platforms team
has created this experiment library
to make it even easier for data scientists
to track and train experiments.
This library provides integration and templates
for MLflow and Vertex Training, giving data scientists
a quick start for treating experiments.
Set up for this library is simple, install via pip,
set up our logging, and set a few variables.
These variables enable better filtering
in ML flow and Vertex Training as well as
providing cost accounting for the resources used
in GCP during the training run.
After setup, you can get started by creating
a new experimentation project.
This creates a new folder structure
with templates generated using the variables that we just
provided.
These templates allow a data scientist
to provide their training code and package into a docker
container, which can be run locally
to test against small data sets on Vertex Workbench or Vertex
Training.
No matter where the containers run,
ML flow is integrated and captures everything needed
to make the run reproducible.
For this demo, raising a simple SK
learned training example with our data
being pulled from a CSV.
Data for training could also be pulled
from Wayfair's in-house feature platform, or Vertex Feature
Store using our library.
Data scientists can easily update this example
to include their custom training code,
which our library will handle packaging into a container
and submitting a training run for.
This library makes it easy to perform training
and experimentation on Vertex Workbench, which
allows for testing on smaller data sets
before you submit to Vertex Training.
For this demo, we'll skip running locally,
and submit the job directly to Vertex Training.
The first step packages the training job
into a docker container, and pushes the built container
to Container Registry.
It also creates the run plan that we
can execute in the next step.
Executing the run plan submits the job
to Vertex Training with MLflow integration enabled.
The variables that we set at the beginning of the demo
are passed as labels to the GCP resources and tags
within MLflow which enables easy filtering and cost reporting.
Now that the job has been submitted,
we're able to monitor the results and see all of the logs
within Vertex Training.
In MLflow, you're able to see all of the training runs
for this project, including any run within Vertex Workbench
or locally.
We can see the metrics for this training run,
as well as the tags that we submitted earlier.
All of the artifacts are also captured,
making it much easier to reproduce this experiment.
This integration makes it easy for Wayfair's data scientists
to compare experiments, and choose
where to continue investing.
This library has enabled Wayfair to accelerate model training
and simplify the adoption of Vertex Training.
After training has been completed,
the model can easily be registered to Wayfair's model
registry and served in production using
our model serving platform.
We are making additional investments
into Vertex Pipelines and Vertex Predict
to make the path to production even easier.
Vertex AI and Wayfair developed libraries have shortened
the time to train a model by 70% to 80%,
and has allowed my team to focus on delivering
an excellent end-to-end development experience,
rather than having to build solutions
for foundational capabilities, such as notebooks and model
training.
For example, a Wayfair data science team
that is working on data extraction from images
was able to reduce their development
to model selection time by 3x, time that was previously
being spent on working with custom built solutions.
We are excited to further our adoption journey with GCP
and additional Vertex AI components,
in particular, Vertex Feature Store for real time data
and Vertex Pipelines for overall integrated flows.
MAYANK GANDHI: Thank you, James.
Wayfair is still at the early stages of a phenomenal growth
trajectory.
And we've been making significant changes
to our infrastructure and platforms
to prepare for the huge market opportunity ahead of us.
Google has been a strong partner on this journey,
and we're looking forward to a continued partnership with them
and the Vertez AI team.
You can follow our journey on the tech blog at wayfair.com,
where the team regularly posts updates about our work
and experiences.
Thank you for having us today.
HASAN KHAN: Thank you Mayank and James for presenting your work
to our audience today.
With this, we end today's session
by thanking everyone who has helped
and listening to the session.
Enjoy the rest of the Next 22.
Thank you.
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