Okay, so now let's talk about Amazon SageMaker.
So SageMaker is a fully managed service
for developers and data scientists
to build machine learning model.
So all of the services so far we've seen
in this section that are all
managed machine learning service with
a very specific purpose,
for example translates some text,
transcribes some audio,
or convert text into audio
or analyze parts of a text,
but SageMaker is a higher level
machine learning service where you have
your actual developers or your data scientists
within your organization create
and build machine learning model.
So it is a lot more involved
and a lot more difficult to use.
Now, when you want to do this kind of processes
to build a machine learning model,
you have to do a bunch of steps that I will show
you in a second,
and all these are quite difficult to do
in one place,
plus you need to provision some servers
to perform these competitions to create these models,
and that can be cumbersome as well.
So this is where SageMaker comes in.
So SageMaker will try to help you all along the way
for the process.
So I will show you what machine learning actually is,
and this is a simplified version,
if you are a machine learning expert
don't get mad at me please,
but let's say you wanted to build a model,
or let's say I wanted to build a model
to predict what score you're going to get
at your certified CLAP practitioner exam.
So how will I do it?
Say for example that I am a developer
or a data scientist,
so I'm going to gather all your data from
the actual performance of my students.
So I will ask maybe 10,000 students
to give me information about how many years of experience
in IT they had,
how many years of experience with AWS they had,
how much time they spent on the course,
how many practice exams they did, etc. etc,
so I'll try to gather as much data as possible,
and then I'm going to label the data.
So that means you need to say which columns
corresponds to what,
and also you need to give some kind of score,
and the score is the actual score for me
at the exam that someone obtains.
For example someone did not pass,
670, maybe he didn't do the course completely,
that would be the reason.
Maybe someone passed with high grade,
so 990 or maybe someone with an even higher grade 934,
so, each student will get a specific score,
and my guess is that based on the data that I've collected
I can predict what the score will be.
So I've first done the labeling,
and that labeling process can be quite complicated to do
in practice.
Then I need to build a machine learning model,
which is how can I predict these scores
from the historical data.
So, then you build that machine learning model
and then you have to train it
and tune it.
So this is another part that's actually quite difficult
to do, which is how to refine my model over time
to better fit my data and my outputs.
Okay, so now SageMaker and all of this will help
you with the labeling,
the building, the training
and tuning, but not only.
Now we have a machine learning model
and it is created,
it's fully working.
So now I need to use it.
So this is called deploying machine learning models.
So, we're going to get new data coming in.
For example you are the new student,
and I'm going to survey you,
and I'm going to say okay,
how many years of experience do you have in IT,
with AWS, how much time have you spent on this course,
and then I will apply based on this data
you just given me,
I will apply the machine learning model that I have created
from before.
And then the machine learning model will say,
for example, hey it sends based on the data you have
I'm going to predict that this student
will pass with a score of 906.
And this whole process,
of labeling, building the model,
training it, tuning it,
applying it can be all done within SageMaker.
So, that's it for a quick introduction,
but I hope you liked it,
and I will see you in the next lecture.