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‫Okay, so now let's talk about Amazon SageMaker.

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‫So SageMaker is a fully managed service

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‫for developers and data scientists

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‫to build machine learning model.

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‫So all of the services so far we've seen

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‫in this section that are all

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‫managed machine learning service with

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‫a very specific purpose,

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‫for example translates some text,

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‫transcribes some audio,

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‫or convert text into audio

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‫or analyze parts of a text,

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‫but SageMaker is a higher level

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‫machine learning service where you have

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‫your actual developers or your data scientists

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‫within your organization create

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‫and build machine learning model.

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‫So it is a lot more involved

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‫and a lot more difficult to use.

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‫Now, when you want to do this kind of processes

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‫to build a machine learning model,

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‫you have to do a bunch of steps that I will show

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‫you in a second,

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‫and all these are quite difficult to do

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‫in one place,

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‫plus you need to provision some servers

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‫to perform these competitions to create these models,

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‫and that can be cumbersome as well.

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‫So this is where SageMaker comes in.

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‫So SageMaker will try to help you all along the way

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‫for the process.

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‫So I will show you what machine learning actually is,

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‫and this is a simplified version,

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‫if you are a machine learning expert

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‫don't get mad at me please,

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‫but let's say you wanted to build a model,

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‫or let's say I wanted to build a model

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‫to predict what score you're going to get

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‫at your certified CLAP practitioner exam.

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‫So how will I do it?

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‫Say for example that I am a developer

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‫or a data scientist,

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‫so I'm going to gather all your data from

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‫the actual performance of my students.

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‫So I will ask maybe 10,000 students

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‫to give me information about how many years of experience

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‫in IT they had,

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‫how many years of experience with AWS they had,

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‫how much time they spent on the course,

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‫how many practice exams they did, etc. etc,

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‫so I'll try to gather as much data as possible,

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‫and then I'm going to label the data.

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‫So that means you need to say which columns

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‫corresponds to what,

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‫and also you need to give some kind of score,

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‫and the score is the actual score for me

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‫at the exam that someone obtains.

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‫For example someone did not pass,

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‫670, maybe he didn't do the course completely,

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‫that would be the reason.

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‫Maybe someone passed with high grade,

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‫so 990 or maybe someone with an even higher grade 934,

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‫so, each student will get a specific score,

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‫and my guess is that based on the data that I've collected

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‫I can predict what the score will be.

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‫So I've first done the labeling,

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‫and that labeling process can be quite complicated to do

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‫in practice.

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‫Then I need to build a machine learning model,

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‫which is how can I predict these scores

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‫from the historical data.

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‫So, then you build that machine learning model

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‫and then you have to train it

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‫and tune it.

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‫So this is another part that's actually quite difficult

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‫to do, which is how to refine my model over time

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‫to better fit my data and my outputs.

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‫Okay, so now SageMaker and all of this will help

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‫you with the labeling,

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‫the building, the training

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‫and tuning, but not only.

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‫Now we have a machine learning model

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‫and it is created,

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‫it's fully working.

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‫So now I need to use it.

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‫So this is called deploying machine learning models.

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‫So, we're going to get new data coming in.

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‫For example you are the new student,

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‫and I'm going to survey you,

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‫and I'm going to say okay,

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‫how many years of experience do you have in IT,

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‫with AWS, how much time have you spent on this course,

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‫and then I will apply based on this data

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‫you just given me,

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‫I will apply the machine learning model that I have created

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‫from before.

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‫And then the machine learning model will say,

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‫for example, hey it sends based on the data you have

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‫I'm going to predict that this student

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‫will pass with a score of 906.

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‫And this whole process,

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‫of labeling, building the model,

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‫training it, tuning it,

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‫applying it can be all done within SageMaker.

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‫So, that's it for a quick introduction,

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‫but I hope you liked it,

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‫and I will see you in the next lecture.