OK, so in this next section of the course, we are going to look at what I think is an exciting opportunity
to use industrial strength time series analysis.
Now, usually when we do machine learning, we think about building the model ourselves.
This is what most data scientists in the industry do.
But what if you don't have data science or machine learning expertise on your team or what if you do?
But they don't have experience in this domain?
Well, luckily, there are services out there that you can use.
This section is all about Amazon Web Services, also known as eight of us.
So if you've taken my courses before, you already know that eight of us is a behemoth when it comes
to cloud computing, we've used Adewusi to run large scale, mass produced jobs to create powerful GPU
instances for training deep neural networks and more.
Now, you might think of eight of us as just a service that provides you with virtual machines so that
you can run websites or train machine learning models or do big data processing.
But in fact, Adewusi does a lot more.
In particular, it offers pre-built machine learning models.
I'd encourage you to check out their services for yourself so you can see the wide range of applications
they offer, from face detection to speech recognition to sentiment analysis to neural machine translation
and yes, time series forecasting.
So this section is all about how to do Time series forecasting with Adewusi.
Now, some machine learning services on Adewusi offer you pre trained models, so all you have to do
is call a function and you get an answer.
For example, if you want to do sentiment analysis, you can just call a function in the Boto three
library, passing your text and get a response, telling you what the sentiment of that text is.
As a quick quiz question, let's think about why that would not be the case for a time series forecasting.
OK, so hopefully you thought about why times for his forecasting isn't as simple as just calling a
function in Boto three.
The answer is, of course, that the forecast for your Time series depends on your Time series.
Of course, Amazon hasn't necessarily seen your Time series before.
This is unlike something like sentiment analysis where your text is probably very much like other text.
Amazon has seen.
So in order to make a forecast on your data, you have to actually train a new model.
As you'll see, this is quite an involved process.
That's why this is an entire section of a course and not just a lecture.
In fact, I would say it's more difficult than what we did previously in the course.
The upside is that you do get to work with a powerful industrial strength time series forecasting product.
So who might be interested in this section?
Well, let's say your boss asks you to use Amazon forecast.
Suppose that the senior leadership at your company feels more comfortable using Amazon's solution instead
of a custom made solution.
Another reason you might want to use Amazon forecasts is that you've taken this course and you've realized
that data science and machine learning is way over your head.
You might be a software developer who's not used to writing algorithms from scratch, but rather using
enterprise products purchased from other companies.
In this case, you would feel right at home with Amazon forecast.
Another reason you might want to use Amazon forecast is that you've tried everything, but your model
isn't as accurate as you like.
You might want to try Amazon forecast to see if it can do better.
Another reason to use Amazon forecast is that it does automate and hyper parameter optimization for
you, therefore, you don't have to think about those things yourself.
So if you don't want to worry about choosing the right model or searching for the right hyper parameters,
then Amazon forecast offers to take that work off your hands.
Now, there are also some reasons why you might not want to use Amazon forecast for one, this is not
free.
You do have to pay for that auto Amelle and that type of parameter optimization.
Every time you make a forecast, you have to pay for that.
Every time you train a model, you have to pay for that.
When you store data in three, you have to pay for that.
So computation and data storage costs money.
Another reason you might not want to use Amazon forecast is that it can be quite a bit slower than what
you're used to.
You've already seen how long it takes to train a CNN or an aunt in or in a remote model.
What you're about to see is that the whole process for building and training a model with Amazon forecasts
is surprisingly slow.
Yet another reason you might not want to use Amazon forecast is you're about to see how complicated
it really is.
This is not because the modeling we're doing is particularly complicated.
It's because how the API works is complicated.
So after seeing how to use it in this section, you may decide that it's not for you.
But again, that's the tradeoff you have to make.
Yes, the API is a bit more complex, but you do get a really powerful model with lots of complex features.
So here's a quick outline for this section.
Now, this course is mostly focused on the data science side of things.
I'm going to assume a certain level of competence with programming, reading documentation, using Web
services and so forth.
So this will not be a total beginners introduction for how to sign up for an account with us, how to
get your credentials and so forth.
I'm going to assume that if you are interested in working with Amazon Web Services, that you have the
capability of an average person who actually uses Amazon Web services.
Also note that if you work at a company, the sign up process and your credentials will probably be
handled for you.
So once you actually have an account and you know how to use it, we'll start by creating an email specifically
for use with Amazon forecast.
I am.
Rules are basically Amazon's way of handling security and authentication.
Next, we'll look at how to upload our data to us.
Three as three is another Amazon Web service use to store data.
This is where a model will look for its training data.
The next step will be to build and train our model.
Once we've done that, we'll see how we can use our trained model to make forecasts.
Finally, we'll look at how to evaluate how good our model is, one interesting thing about this process
is that at a high level, you can see that it's pretty much exactly the same as the first section of
this course.
Get the data, build the model, train the model and evaluate the model.
As you recall, that ended up being only a few lines of code.
So what's different now?
So although the high level steps are the same, the actual syntax in API is very different.
So that's the tradeoff.
Yes, you do get to use an industrial strength time series forecasting solution, which may or may not
be better than just a basic Python library, but each of these steps take significantly more work.
To prepare for this section, you'll need to install the Boto three library as usual, you can use the
PIP install command.