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HENRY TAPPEN: Hello, everyone and welcome to the session.
We hope you've been enjoying the Applied ML Summit so far.
I'm Henry Tappen, group product manager for Vertex AI,
and I'm joined today by Cengiz Ucbenli the global head of Big
Data and AI at Vodafone.
CENGIZ UCBENLI: Hi, everyone.
HENRY TAPPEN: And we're really excited to tell
you more about how Vertex AI helps you get models
into production via ML.
Now if you've worked on machine learning problems
for any period of time, you've probably
realized that getting a model into production
is vastly more difficult than just
getting it run on your laptop.
The source of so many challenges is
that machine learning, when used in the real world,
needs to be able to react to continually changing
circumstances and novel situations
that you just don't encounter until you
try to use it in real time.
If you've ran into these struggles, you're not alone.
The IDC found that only 36% of the surveyed enterprises
that they consulted had succeeded in putting completed
models into production.
Vertex AI's MLOps suite is designed
to help you overcome the challenges with production ML.
It aims to make your machine learning processes
resilient to the type of volatility
seen when using AI to solve real business-critical problems.
Vertex AI does this by helping you
move from one-off manual processes
to regular automated workflows, including data processing,
training, and deployment.
With your model automatically retraining and redeploying,
you can be sure that they're up-to-date with whatever's
changing in the business problems
that you're trying to solve.
Last year, Vertex AI delivered its first set of MLOps tools
with the launch of Vertex pipelines and Vertex AI Feature
Store.
Both of these components help you orchestrate the many tasks
involved in training and pushing a model
so that you can automatically and safely rerun them
whenever new data comes in.
This year, now that we see many more models
reaching production, we're building new tools
to help you maintain high model quality.
Our new monitoring and governance tools
will help you catch issues before they become problems
and, when you do find them, solve them.
So why don't we talk through a few of them?
To start, when developing new versions of models,
you want to make sure that you're continually improving
over previous versions.
Vertex AI's Experiment Tracking allows
you to capture and compare metrics
from various model runs.
With it, you can chart the accuracy of your models
over time as you rerun pipelines or try brand-new approaches.
Vertex AI Experiment Tracking also
integrates with Vertex AI's Serverless TensorBoard.
So when you see something interesting,
you can easily dive in and get more information about it.
Once you get to regularly updating models,
managing the sheer number of artifacts
can quickly get out of hand.
That's why Vertex AI Model Registry is there
to help you with the chaos.
Vertex AI's Model Registry organizes your model artifacts
by versions.
Once you've registered a model, you
can easily push new versions out to model consumers
without them having to update any of their code.
Vertex AI's Model Registry is designed
to work with any type of model and deployment target,
whether that's through BigQuery, Vertex AI,
custom deployments on GCP, or even out of the Cloud.
And finally, as with any system that you're depending on
want to know that your machine learning models
are working correctly and, if not, to know how to fix them.
Vertex AI's Model Monitoring suite automatically
analyzes the data flowing into and out of your model
while it's deployed.
It looks for distributional drift over time
and can alert you when it finds discrepancies,
along with a hint as to what you might need to fix.
Model Monitoring helps you keep an eye
on other parts of the ML process,
including looking for problems with your batch predictions
or with data being fed into the feature store.
Now we realize that success in machine learning
depends totally on success in data processing.
That's why we've designed these and other MLOps tools
to fit into Google Cloud's broader data portfolio.
Vertex AI gives you total flexibility
in the tools that you use to complete your machine learning
journey, whether that is specialized
services like DataFlow or DataProc or even
generic Python code.
You can easily mix and match different tools
as you compose entire ML workflows.
In fact, just recently, we rolled out
special support for BigQuery ML throughout the whole platform.
You now have, for example, pipelines components
that can help you orchestrate complete BigQuery training
and deployment steps, like training, evaluation,
and otherwise.
Or you can manage your BigQuery ML models
via the Model Registry.
Now to tell you a bit more about how these tools can
work on real problems, I'm going to invite up Cengiz
to tell you a little bit more about how
Vodafone is applying Vertex AI.
CENGIZ UCBENLI: Thank you so much, Henry,
for the opportunity.
I would like to introduce Vodafone a bit
with some numbers.
So basically, Vodafone is a global telecommunication
operator, operating in more than 21 markets
and serving more than 300 million customers.
Vodafone has a three-layer strategy.
We would like to optimize our core businesses
and, eventually, we would like to move
beyond connectivity business and establish new areas to generate
certain new revenue streams.
I would like to also show you, in the next screen,
our Vodafone AI strategy.
Basically, we examine various industries--
adjacent industries, some mobile technology companies--
and try to understand our gaps in the area of AI.
We end up with three pillars to basically identify
our overall strategy.
The first one is real-time AI.
So basically, it's all about using real-time streaming data
to make timely predictions about customer churn, customer NPS,
or upsell/cross-sell opportunities.
And also, the second one is AI at scale.
It's all about, basically, building once,
deploying many times in various markets
so we can reap the benefits of economies of scale.
And also, AI everywhere-- this is also
another critically important pillar for us
because we have various type of business functions
that can benefit from the power of data and AI.
So this is also a critical pillar for us.
So basically, when it comes to AI everywhere,
there are five dimensions we are generally
focusing about reaching out to customers,
finding the right customer, making the right offer
through the right channel on the right time,
and also with the right message.
So all areas can be optimized by AI algorithms.
That's why applying AI algorithms
in various facets of the business is so critical for us.
So this is a long journey for us.
We started with some fragmented big data and AI
solutions with [INAUDIBLE] clusters in the past.
Right now, we are trying to basically unifying our platform
infrastructures over cloud-based solutions.
I would say Vertex AI is the pinnacle
of that journey for us.
So basically, it brings us this power of scalability.
We built a template in certain markets
and scaled it over to other markets.
This is one of the key feature of AI Booster, where
we benefit from in Vodafone.
And also, the other important critical area
is that zero ops and zero delays in our products [INAUDIBLE]
pipeline, and also leveraging Google's novel
prefabricated models is one of the big plus of AI Boosters.
Since I mentioned the real-time AI, AI everywhere, AI
at scale-- of course Vertex AI is
doing a good job stitching all this together
with the right talent.
So in order to basically achieve that,
thanks to Google's all support, we've
been able to basically train and educate our data scientists
and basically turn them into a blend of data scientist
and machine learning engineer so they can basically build a PC
and basically put that PC into the production by themselves
without any assistance.
So this is a great step forward for us
to accelerate the overall AI journey here in Vodafone.
HENRY TAPPEN: Well, thank you Cengiz,
and I'm glad to hear we're able to help you out.
Well, there's so much more to hear about
at the Applied ML Summit.
We hope you guys continue to enjoy this
and other presentations for it.
Thank you very much for joining us today.
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