[MUSIC]
>> Hi friends, I am super excited for this stream
because I've heard a lot about Azure
and I've actually never had a chance to use it
but it's one of those skills that when I'm looking for jobs
it's constantly on the list of skills
that people want from data science people and like ML devs.
And so, and this is a great, a great chance to sit
down and figure out what, what even is Azure?
How does it work? What's available?
Unlike a lot of my machine learning streams where
I'm teaching you something, this stream is going to
be us learning together because I've never
I've never used Azure myself and I'm new to it as well.
All right, so I started just by looking
through some of the options on Azure.
And so there's a couple of different options
that I looked at. there's one that is more code oriented.
There are of course, Jupyter notebooks, what place
doesn't have Jupyter notebooks.
There is some really cool stuff with the studio.
And so that's where I thought to start today
because it seemed like the fastest way to kind
of get a project up and started really quickly.
Azure ML Studio is what it's called.
One thing that is really cool, that I didn't know is
that they give you like $200, I think,
of free compute and resources.
So we're going to have, we're going to have a little bit
of some, some dollars to play with, which is going to
be nice, I wanted to kind of show you all
from the ground up how to really get started.
Other than that, like, all I did was click start a data
analytics project, and this is where we are.
So you are jumping in right at the start with me.
I don't know Databricks, to be honest.
So I was kind of thinking that this,
this seemed more in my wheelhouse personally.
So I wanted to get started with this and let's see
there's a studio, I think this is this, okay, here we go.
Yes, let's create a work workspace.
Okay, a foundational resource in the cloud.
Well, this looks like a different, different place.
So let's just actually go back
to the start, create a resource.
Let's do that, machine learning, create, yes.
I want to create a, a machine learning please.
A singular one, one machine learning, please.
So create new resource group, provide the following
information to configure your resource.
Let's see workspace name, so let's just call this
like day one, is there a dark mode settings?
Oh, oh, there we go, I too am a major fan of dark mode.
So let's see here, we need to continue
to fill out our workspace, let's get back to the
documentation I was looking at.
So we're going to do tutorial create
a simple classification model
without writing a single line of code.
So we've created our we're working on creating our workspace
and here's where we need these, these different fields.
So workspace name, I was thinking of something sassy.
And then I'll try to be like, I don't know.
We'll just call it day one day and one tutorial.
That's not tutorial, create new.
We'll do basic and just call it again.
Tutorial alpha alphanumeric, okay. So day one tutorial.
I don't need advanced stuff, this is not a high business
impact workspace, now reviewing, create,
create validation past, that's great. I answered everything.
I got all the things I need, okay, so it can take
several minutes, that's fine, we can catch up here.
Let's see, we are deployment is complete.
What next? Let's let's actually follow
the instructions here, okay, like I can just go off
on do it all on my own, yes, please, yes, please.
I would like that workspace or no, we call it day.
One tutorial get started, okay, why is it bright again?
I would like a tour, but I would like the tour to be
in dark mode and the left lane.
Automated ML, okay, new automated ML run.
Yes, so select dataset from local files, that's fine.
Yes, okay, so this is, this is like the, for the tutorial
if you go into the documentation, you'll see
they provide this, this CSV on bank data
for use with this particular tutorial.
And so that's what, that's what we're working with.
And then let's see, skip rows some headers settings
and preview the schema, okay, so we don't want day
of the week, it says that's kind of cool to just letting
people toggle that that's a very intuitive format.
I like that create, yes give me data, make it.
So we have our data now, I think let's see.
Next nope dataset is required.
Cool, enter new experiment, all right.
Here's our, my first auto ML experiment
and select a compute cluster, create new compute, all right.
Virtual machine type CPU, that's fine.
Select from recommended options too.
Okay, next, compute name what should
we name our compute or minimum?
Notice one nodes to zero and max nodes.
I mean, this is really small data.
I think we just need one waffles.
Waffles, I love that, okay, Waffles is our compute.
Poor Microsoft is like what? All right, 120.
We're good there, create our compute target, create it.
So primary metric view, additional configuration settings.
Yes, give it to me, that's what I'm talking about.
All right, so these are the these are the metrics
I was looking for earlier, oh, ooh, this is cool.
I can just be like, nah, I hate decision trees.
Don't use decision trees, that's cool.
And so this is just none, that's fine, validation, yes.
And my hopefully my wonderful machine learning.
Ms. Folks feel comfortable with K fold, cross validation
finish, save finished to run the experiment.
Let's run our experiment, metrics.
Oh, hello, hello, this is really neat.
I love these metrics, this makes me so happy
that there's just so much, oh yes.
Confusion, matrix, let me see it.
I love confusion, major c's, so it did, it did two
different models simultaneously.
And did seven runs, each run of which
was doing five fold cross validation.
Like you would have to set all of that up.
Not only that, but it would take much longer
than it did here, this is what I've been
really interested to see, is a model explainability
run in the Child runs tab, oh, Childs runs go.
Okay, run one backup at the top.
So let's go here, run one, so it did all of these.
That's really cool, all right.
And then select the first one.
It is, it's still doing the explainability run.
All right, nevermind, we want to look at Childs runs.
I think he's cute, all right.
Waffles cluster has one busy nose.
Why is waffles busy? What are you doing Waffles ?
Waffles has succeeded, you're running,
oh yeah, it's finalizing this run
and then it's queuing up all these other ones.
Okay, all right, that's cool.
We can chat, we can hang out while we're waiting
for explainability runs, I'm really interested
to see what the explanations are.
We can see a little bit of a preview of them here.
Here we go, preview of explanations.
Nothing's failed, that's good, so it's still running.
Do, do, oh, here we go, what do they?
Okay, oh, so this is interesting.
Okay, so I think this is some
of the EDA that we were talking about.
That's really cool, this is really, really cool.
I feel like this is, this is really interesting
to me because at least in my mind, right?.
When I do exploratory data analysis
usually that comes before the model selection
and before like the building and the training of the model.
So I would almost like to see some
of this kind of exploration of the dataset more before
even though like they have this auto selection
of model type and things like that.
I still, I think I would like to be able to
explore the data even before I moved
to the training stage, okay, we see the metrics.
So that's really nice, okay, so this is cool to me.
This is also part of explainability.
This is really interesting, now, one thing I did notice,
I don't know if all of you picked up on this too,
when we were just kind of skimming the data
set a little bit, I did see things like typos,
strange formatting and stuff that I wonder
how they handle that, this is really cool.
So it tells me and balanced data we're alerted
to the imbalanced data, what is it going to do about that?
This is really cool, so, okay.
Automated MLS built in capabilities to help deal with it
waiting it, detecting it, probably up sampling.
Yeah, up sampling or sub sampling performance metrics
that deal better with imbalanced data re sampling.
Ah, this is really cool though, because to me
like explainability is, is more than just here's
the numbers, right? Explainability is also this really
well done almost exhaustive documentation.
And that's, that's like another hill that I will die
on is the importance of having really good documentation
and knowledge sharing whenever you're doing kind
of a big machine learning product.
So it makes me really happy actually, is
that they have like, not just, hey, we found this problem
and they leave you to deal with it.
But they also have like a resource that you can go to
about what it's, what it's doing with the problem.
Also cool duration, it's about a minute, that's nice.
All of, all of my machine learning Ms friends,
we'll see, we're scaling our data, tune hyper parameters.
So this is how you do it in the code, because that would
that would be frustrating if there was no way to
hyper parameter tune in the automated ML.
Ah, I missed this there's an explore tab with our model.
So I think there's a lot more that we can take a look
at here, please don't write profile
to be the schema and summary statistics, all right.
So let's generate a profile and we'll use poor waffles.
Whenever waffles is free to do this
but so it looks like there is an option for DA EDA.
I'm surprised that wasn't in the tutorial.
That's, that's good to know that there's
like that explore tab, so it is running.
Let's see, let's update, oh, that's,
that's the data set parsing, I think this is still
doing our explainability but we did get kind of a little bit
of a preview of what the explainability looked like
which is, which is cool and it's good to know.
It's I think it's tough to just kind of say
like explain model and then you talk
about like feature importance and like data relationships
because explainability is so much broader than that
but I love that they have that though.
So we have run all of these different models
and you can see with like varying levels of success, right?
Like I think our worst model, right?
is this about 70, about 80%, but, so this has been really
interesting like with the speed, like each of these models
is taken about a minute to train into evaluate.
I didn't see an option to set
like proportion of train, test validation, split.
But again, like these are these are small details
that are probably because I just followed
this particular tutorial and we didn't really dive
into the details, I like this data guardrails this.
So this is something we found a little bit later
that identifies some of the issues in the data.
So this I thought was really, really cool.
And it like redirects you to read more about how
to address various issues in your dataset, yeah,
really cool, we've kind of gone through
a very, you know, basic tutorial
gotten a little more comfortable with the interface
learned some of the tools and the features available.
Obviously when we dive into some of the bigger projects
it's going to be a lot more for tonight.
This is a really good start like learning
kind of what's available, bye everyone.