‫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.