>> In this special Build Edition of The AI Show,
join us to hear from Aniththa Umamahesan,
Program Manager on the Azure Machine Learning Team.
She will show us how to use automated machine learning while
identifying the best approaches and tools to use.
Depending on your scenario,
you get to be the hero of the story. Make sure you tune in.
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>> Hi everyone. My name is Aniththa,
and I'm a Program Manager on
the Azure Machine Learning Team here at Microsoft.
Today we're going to learn how to be a ML hero using automated ML.
Let's begin. So before we get started,
I want to give you a quick overview of what's
on your agenda for the next couple of minutes.
We'll start off with the introduction to
automated ML and look at the capabilities.
Then we'll dive in to see what's new and look at our roadmap.
Once we do that, we'll go ahead and do
a customer deep dive and a demo,
and then see how you can get started with automated ML.
As we all know, AI is everywhere.
It can be simple as figuring out the price of
a car or figuring out how much money you have in a bank.
As more industries rely on big data to make decisions,
data science has become increasingly
important across all industries.
Just like Cloud computing was
fundamental in the explosion of startups,
machine learning democratization will likely power
the next generation of applications crafted by developers,
but also directly used by consumers and business tools.
So let's take a look at the machine learning process.
As a developer new to machine learning,
I want to simplify and streamline
the model creation process because
building models can be tedious and iterative.
You have to first select features and choose your algorithm,
then set up your hyperparameters,
and then iterate on the data as many times
until you get to that satisfactory model.
As you can tell, this is a time-consuming process.
The current process need practitioners who are skilled in ML,
such as data scientists.
What if we can simplify this process?
What if we can enable those who are novice in
the area to create machine learning models?
What if we can enable
those same skilled professionals to
be more efficient and productive?
That's exactly where automated machine learning comes in.
With automated ML, all you need to do is bring in
your dataset and specify some configurations and constraints.
So for example, you can say you want to run
a classification task for three hours,
and what automated ML does is it intelligibly tests
multiple models in parallel
and returns the optimized model for you.
So automated machine learning picks an algorithm and
hyperparameters for you and
generates a model that is ready for deployment.
The model then can be downloaded to be further customized as well.
Now, let's go ahead and look at
the automated machine learning mission.
Our goal is to enable automated building
of machine learning with the goal of democratizing,
accelerating, and scaling AI.
So let's go ahead and see what this actually means.
So let's just say you have a data science team
with varying levels of experience.
With AutoML, you're able to expedite
the entire data science process and this is
where the democratization AIPs comes in.
Now, if you have a smaller team of data scientists,
with AutoML you are able to make the
most out of these limited resources,
thus improve the productivity of your data scientist.
We have also seen scenarios where customers want to
build multiple machine learning models in parallel.
Now they can do that in an automated way,
and this is what we mean by scale AI.
So now that we know what AutoML is,
let's dive into the capabilities and see what's new.
Automated machine learning is for
everyone who is working with data.
It currently supports classification,
regression, and time series forecasting task types.
If you are a data scientist,
you'll be more comfortable using
the Python SDK in Jupyter Notebooks,
and you'll be able to use Jupyter Notebooks
from the Azure Machine Learning Studio.
If you are a citizen data scientists
or if you just started making your way as
a data scientist or if you prefer
the graphical user interface over coding,
you can use the automated machine learning web user interface
in the Azure Machine Learning Studio,
and start a training job within just a few clicks of a button.
As we all know, data is important in the model creation process.
For example, when we're creating
a predictive model to forecast sales on a retail store,
weather data, and how the data can have an impact.
Now, you can enrich your data with
Azure Open Dataset with data like public holidays,
historical weather, and US population, for example.
In addition to Azure Open Datasets,
we also support guardrails on your data.
So now you can look for things like
class imbalance or high cardinality columns.
Featurization is another important step in building your model.
AutoML currently supports
automatic visualization and feature engineering.
In addition to the auto featurization,
you can also customize your featurization.
For example, you can do stuff like blocking
your transformers, overriding column purpose,
choosing to include or exclude columns in training,
and deciding how to impute missing values.
In addition to the customize featurization,
AutoML also supports deep learning with BERT and BiLSTM for NLP.
After featurization comes training.
We have added world-class statistical models
like ARIMA and Prophet,
and deep learning models like TCN.
We try all these along with the classical models
on our user dataset to provide better performance.
So after training, we often hear two questions.
What did you do and how are the predictions made?
So to answer the first question on what did you
do and how did AutoML actually build it,
we currently support featurization transparency.
So we show transformers parameters and
any values we altered in the dataset along with the algorithm.
To answer the second question on how are predictions made,
AutoML currently support model interoperability.
So you can see which features played
an important role in the model creation process.
So now, the model creation process is no longer a block box
as it is transparent and you
get a better understanding of your models.
Many models is another thing that comes up when building models.
Often people want to build multiple models in parallel,
that be tens, hundreds,
or even thousands of model.
Now, instead of having
one model to forecast for your entire store,
we are introducing grouping to our many model solution,
which is a first step towards hierarchical time series.
This will allow for more detailed forecasts.
So now let's go ahead and see what's on
the roadmap for the next 6-12 months.
To start off, in order to improve
our transparency and customization story,
we will be supporting code generation,
allowing you to take an experiment from
the UI or an individual iteration,
and generate code out of it in order
to further customize your model.
The next thing is componentize featurization,
allowing you to use featurization of your own models.
We also are looking to support bringing your own model,
and also a many models authoring UI experience,
allowing you to build multiple models right from the UI.
Finally, we have some forecasting enhancement
with hierarchical time series support on our roadmap.
So now let's go ahead and do a customer deep dive.
So we have many customers currently using AutoML,
and this slide highlights a few of them.
Today we will dive deeper into
an interesting use case coming from TalentCloud.
TalentCloud is a smart agriculture solution provider that puts
centers in their agriculture field to
track and manage environment information.
Their customers include 5,000 farms across 210 cities.
Let's see how TalentCloud use AutoML to improve
the accuracy of their AI models and
expedient their AI development process.
So right now we're in the Azure Machine Learning Studio
under notebooks in the authoring tab.
This is TalentCloud's notebook.
So as you can see, the started off by setting up the experiment,
so they imported all the required packages,
and then they gave their experiment a name,
in this case, is cooling.
Once they completed that,
they went ahead and attached
a compute to their experiment, in this case,
they're using a Standard DS12 V2,
which automatically scale to the experiment needs.
After setting up their compute,
they went ahead and they loaded their data, in this case.
You can see the first couple of rows of their data over here,
and in this case, we're predicting on is cooling.
So once they have their data loaded,
they could go ahead and set up their AutoML configuration.
So what they did over here is that they specified the task type as
classification and the primary metrics that they want to
optimize on is average precision score weighted.
AutoML supports additional configuration settings
based on your experiment needs,
which you can explore by looking at our docs.
After setting up their AutoML configuration,
they went ahead and launched their remote run,
and I'll show you how this looks like
from the Machine Learning Studio.
So right now this run is still running.
We can see that we are specified a classification task type,
and we can also see all the other run settings
specified for this experiment.
In addition to the run summary,
we can also go into this data guardrails tab
to see the guardrails placed,
in this case, there's class balance detection
and high cardinality feature detection.
We could also see all the models being explored for
this individual experiment as
AutoML's intelligently testing multiple models in parallel.
So I'm going to go ahead and open up
an experiment that completed a little earlier,
and right now we can see the best model summary page,
which is the recommended model,
in this case, voting ensemble.
We can see that the metric for this was 0.999.
So we could also explore additional metrics like accuracy and
AUC macro to better understand our model.
If we are not happy with the recommended model,
we can always explore one of
the other models from this run, in this case,
I'm going to click on this "LightGBM" model,
and if I click on the visualizations tab,
these are visualizations to
not only help us better understand our model,
but also decide if this model is good enough.
Once we're happy with our model,
we could go ahead and deploy it out to production.
So with AutoML, TalentCloud can automatically
optimize its AI algorithm model do a large number of data samples,
reducing the algorithm training time from
over one week to less than 48 hours.
The solution also had a larger business impact
as it provided higher efficiency,
lower costs, better environment, and safer food.
Now that's the power of automated ML.
Thank you for watching this video.
To learn more about AutoML,
please visit our website and try it for free at ml.azure.com.
Thank you.
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