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>> Hey, my name is Nilesh Acharya.
I'm a Senior Program Manager
working on automated machine learning.
In this video, we going to see how you
can use automated machine learning
to help build an accelerate building machine learning models.
Let's look at a typical example of how you would build a model.
Data is generally in disparate places,
you bring that data together,
and prepare for model training.
You then go ahead and train a model,
and once you are happy with the model output that you've got to,
you go ahead and deploy it.
Now, this whole process is a number of steps and
interconnected decisions that you would make to
get to the model accuracy that you're looking for.
For example, when you're preparing data,
you may ask yourself "How do you handle knows?
Do you have the right number of features in that data-set to
prepare a model for the accuracy
and scores that you're looking for?
When you're building in training,
what algorithm should you select?
Should you be selecting a linear regression or decision tree?
What are the hyper-parameter that you need to
choose for those specific algorithms?"
So as you can see, there's a number of questions you
may be asking yourself through this process.
Let's examine this with a specific example.
If we were to predict how much is a car worth,
we may look at the features that we need to
predict and build the model,
the algorithm that we need to choose from,
to the choose and select,
and the hyper parameters.
So let's start with mileage,
car brand, and year of make.
We then select a gradient boosted algorithm and
its relative hyper-parameters associate to it.
We then get a model score of 30 percent.
Not being too happy with this,
we go and retry with some more features,
a different algorithm, and a new set of hyper-parameters.
We do see an incremental increase reaching 50 percent accuracy.
Again, not being too happy with this,
we go ahead and retry this whole process.
We continue trying over and over
again until we get to a score that we're happy with,
let's say 95 percent.
We then reach, have reached a score that we now
have a model that we going to go ahead and deploy.
As you can see, this process is very iterative, taking time,
and it's guessing to where you need to go next.
So what if there was a way to automate that process?
Yes, there is. That's exactly what
automated machine learning does.
With automated machine learning,
you enter your data,
you define your goals,
and you apply your constraints.
Automated machine learning builds an end-to-end pipeline that
allows you to build a model and reach
an accuracy so quickly and effectively.
You can get an optimized model with
far fewer iterations and far fewer steps,
saving you time, money, and resources.
This helps you scale out and build more machine learning models
with greater speed and accuracy.
Automated machine learning basically simplifies the process of
generating models tuned from
the goals and constraints that you define,
such as the time for the experiment to run,
which models to allow or deny,
how many iterations to run,
or an exit score that you may have defined.
Automated machine learning isn't a brute force attack,
nor is it doing hyper-parameters swing.
Automated machine learning is using machine
learning to help you build machine learning models.
Based on Microsoft research
and work done from our engineering teams,
automated machine learning examines
the data-sets that you have in the characteristics
and recommends new pipelines
to use to build your machine learning model.
This encompasses pre-processing steps of feature extraction,
feature generation, model selection, and hyper-parameter tuning.
Automated machine learning also learns from
the metadata from your previous iterations to recommend
new pipelines to get to your scores
and exit criteria much quicker and much sooner.
This helps accelerate your ways of building
machine learning models with more efficiency and ease.
To get started with automated machine learning,
please refer to our documentation,
sample notebooks on GitHub and blog post.
I hope you enjoy using
automated machine learning and thank you for your time.
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