>> Hey, good news. We're here with
another episode of the Azure Enablement Show.
Today, we're going to be joined by Jason and Sudha,
where we're going to be talking about
Machine Learning Specific Assessments. So join us.
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>> Welcome to the Azure Enablement Show,
where we'll be discussing the challenges you
and our other tech-savvy customers have encountered.
Together, we'll be talking with experts to
find out how they think about these problems,
recommended tools, and best practices,
and tips they've learned from
years of experience that you can use.
Today, we're going to be talking
about Machine Learning Assessments.
I think the best thing for me to do is to switch to customer mode.
Well, here I am as a customer,
and now I have the chance to talk to Jason and Sudha.
Hey, Jason and Sudha, thanks so much for
getting on a call with me. I really appreciate it.
>> Hey, David.
>> Here's my problem. I'm wondering if you can help me with this.
We are just getting started in deploying
some machine learning workloads onto Azure,
and this is our first time
doing this and I want to make sure we get things right.
We've been trying to read the documentation,
but there's a lot of documentation.
I don't really know where to start.
Do either of you have any suggestions on what I should be doing?
>> Of course, David. Microsoft provides
all sorts of guidance for these type of issues.
So we are looking at customers like
you to figure out like how to have a best journey in Cloud.
You know, you have to avoid these costly pitfalls,
improve your operational excellence,
reliability of your system,
the performance, and be secured with your workload.
We are bringing this thing called Well-Architected Framework,
which addresses all these considerations
across the five pillars that I talked about.
More importantly, this is a self-service tool.
The customers can actually
go to our public docs site and they can do
the assessments and they can find
out the recommendations that come out of that.
These are workload-specific as well.
I mean, we have one for Azure machine learning with the data being
exploding and everybody wants to get insights from the data.
Azure machine learning is one of our key servers and we are
providing this workload-specific assessments
that help the customer.
I think you are aware of these RAF assessments, right?
>> Yeah, so I've definitely heard of
the Well-Architected framework and
I've definitely done those assessments.
That thing where user go on to
a website and it asks you a bunch of
questions about the things you said: security,
reliability, performance, cost.
I remember we had to answer,
there's five of these things,
but I guess the thing that I don't really quite
understand is I did that.
For workloads, in general,
what's a workload-specific assessment,
what's special about but ML assessments?
>> Yeah, that's a great question, David.
Yes, this is the new thing that we are bringing on
top of the existing assessments,
which is more general like Azure platform assessment.
Now we are bringing like workload-specific.
What that means is you can go and
look at Azure machine learning specific questions.
Like, for instance,
you are thinking more about ML Ops capability,
or you're thinking more about how do I save
costs using the Azure Machine Learning Service,
or, "Hey, I want to
incorporate like responsibility AI practices,
how do I get guidance for all these specific workload questions?"
We have the Well-Architected framework workloads
specific assessments
that give all these recommendations for you.
And then you can actually cater them to your requirements.
We have about 80 plus recommendations
that we have compiled into these five pillars.
But the best thing is not all of them,
of course, will be relevant for you,
but you can pick and choose which one is the
most suitable for your architecture, your environment.
These are curated guidance coming from
Microsoft recommendations that will help
you get your workload performant. Isn't that good?
>> Yeah, I like the idea,
but like so now my head is really
deep into the ML space right now,
like I'm working on it right now.
What are some of the things that are specific to
machine learning that I would expect in the assessment?
That it didn't do before when I
was just doing a regular assessments?
>> Do you want me to jump in here and answer this one, Sudha?
>> Yeah, of course, Jason.
>> Awesome. David,
let's think about what's
specific about machine learning workloads.
There's a few things.
But maybe the most obvious is that we have
a training phase and we have a using phase,
the time when we're doing inferencing,
where were actually using our model.
That's two very distinct steps
to take in your application architecture,
two very distinct things.
When we're doing an assessment about your application,
we're going to start asking you if you've
considered security for your training session.
Have you considered security?
Have you considered performance
optimization for your inference phase of things?
Those are going to be different.
Think about, for instance, our training.
We often don't need that to be near real-time.
When it comes to that cost optimization,
you may not need the most real-time, most instant response.
Instead, you might be able to take
advantage of things like spot instances.
You might even be able to scale down.
Now, of course, you're also working with
giant datasets when you're doing training,
so there's some tradeoffs there.
Security also can be a little different
when you're thinking about your training phase because
often you can do that entire step inside your network.
Basically completely firewall off in
the same way that all your internal communications are.
That doesn't need to be exposed to the world.
Contrarily, your inference timer when you're using your model,
maybe that model is being used via
an API from a mobile application for your customers,
or maybe just directly from a website.
Now, in that case,
we may want that to be near real-time performance,
so it's a different set of considerations.
Of course, now it's exposed to the Internet.
Security considerations are different.
As we go through and ask you about those considerations,
they're going to be targeted.
The questions and the recommendations
that we're going to give you based on
your answers will be targeted to those different phases.
Unlike, say, if we were asking you about a web app
or your database. That makes sense?
>> Yeah, that makes total sense.
Now I understand the questions I'm going to
be asked and how they apply to my workloads.
What's the payoff here?
What recommendations am I going to see?
Like presumably other people have done this before?
You must know what other customers get for recommendations.
What would you expect the things I would probably see is
just that someone running machine learning workloads.
>> Yeah, I can answer that, Jason.
You're like, David, we have run these assessments with
several customers and very like you,
they're very generally interested more on
enterprise readiness like what does Jason talked about.
They want to make sure their workload is very secure,
that data is encrypted,
they have the relevant private endpoints, things like that.
The key hot topic they're worried about is ML Ops.
This is big transformation for them
bringing their data scientists and the IT engineers.
How do they make this machine learning
model work well with the data geeks,
with the IT landscape. That is a key thing.
Those type of guidances are important.
The other important thing that customers like you are
interested are more on the model maintenance best practices.
Like how do they understand drift in the model?
How can they retrain the model?
How can the influencing that Jason talked about can be
real-time without much of the downtime of retraining.
The data changes, right.
Responsibility. This is really picking up like a huge hot topic
these days because they want to
know the transparency of the AI models,
fairness like inclusiveness,
and all of these aspects of responsibility.
Those are some of the key topics,
and the last, but not the least,
is the compute, the cost.
That's the main thing.
What Jason talked about the training and the inferencing,
and they always have this guidance like how do I train my model?
How can you lower my cost?
How do I inference?
It could be a deep learning or a machine learning model,
it can be different type of influencing needs you need,
and these type of guidances are supercritical for them,
and this is the relevant recommendations that we are seeing,
like most often asked by these customers, like you.
>> That's cool. I guess
I'm getting a really good sense of what I have to do.
Another thing I'm curious about is this assessments is new to me.
I took the other assessment, but this wasn't
available when I was first doing assessments.
That sounds to me this is coming on board
and this is the first version of it.
What more am I going to expect
to be showing up in assessments like these?
>> Oh yeah, of course.
I mean, they would be are always
interested in bringing more to the customers.
That's just our job here.
First off, these assessments are kept evergreen.
When the product changes, cycle changes.
The ML assessments will be
updated to include the new features for the customers.
The other thing is these are assessments that are
questionnaires that brings you
a concept of how do you define your architecture.
Maybe your existing architecture it
brings you different recommendations.
We are also adding what is called as
advisor signals, which are basically,
think about them like
a service consultant which
is available to you-all the time at your service.
What this does is it's really like it's a service on top of Azure.
It provides you health recommendations
about your service. In terms of cost.
You didn't shut down that VM right,
or this particular endpoint is not secured, things like that,
that come to you with your subscription,
and that's the next addition of ML.
We are also continuously
improving and adding more workload-specific assessments
like we can expect to see
the other AIML services like Cox Services, the Cox search.
All of them are cooking
and it'll be coming to the customer very soon.
Those are something that you can expect to see in the future.
>> Cool. Well, let me ask the obvious question.
Now you've definitely piqued my interest,
and now I have a sense of what's available here.
What should I do next?
>> Jason, you want to answer that question for David?
>> Yeah, by all means.
I think what you want to do is head over
to our assessments website
and make your appropriate selection to say that
the bill workload that you're interested in is right now.
Of course, your ML workload,
and that's going to kick off everything we've just
talked about here for your application.
>> Cool. Well, I think I'm going to go do that,
and I think with that, I'm going to come out of customer mode.
Thank you both.
I really appreciate your help.
Let me go back to being a host.
I'm back as a host.
Well, I just want to thank Sudha and Jason for being on the show.
I want to thank all of you for watching.
I hope that helped you and will be useful to you in the future.
Please join us again on
more episodes of the Azure Enablement Show.
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