Cloud platforms are crucial for data science.
Today I'll give you an introduction to using the cloud for data science.
And make sure you watch till the very end,
where I'll share a couple of learning resources that I use to keep myself up to date with the latest cloud platforms.
And this will help you set yourself apart when you applying for data science jobs.
I'm Samanvay, a data scientist living in Sydney, Australia.
A while back, I talked about the best laptop for data science.
And if you recall correctly, I recommended laptops without a GPU.
Which is obviously essential for machine learning algorithm.
So turns out that, in the commercial data science world,
You'll rarely use your personal laptop for training models.
In fact, most of the time you won't even have a training data set on your local computer.
Everything in the commercial world is done on the cloud.
A cloud is basically a range of computers and services, that you access over the internet.
These computers usually sit in super-secret locations under high security.
Just imagine a whole array of computers in the massive warehouse,
With state-of-the-art colding technology and no monitors.
There are three benefits to using the cloud.
The first is that you have access to a large amount of computing powers and storage
along with a number of services with the click of the button.
The second is that there's generally cheaper per unit of power than your local computers.
Due to economies of scale.
The third is that the cloud is more reliable and secure than your local computers.
And this is especially useful when you are trying to train models over one or two weeks.
According to the 2021 Stack Overflow developer surveys,
The top 3 most used cloud platforms are Amazon Web Services, Google Cloud Platform, and Azure.
Together they own the majority of the market share when it comes to cloud technologies.
Personally, I've used Amazon Web Services and Google Cloud Platform.
And comment below if you've used any of these technologies.
Now let's talk a little about what I've used the cloud for,
so that it gives you more of a practical idea on how it can be used in data science.
The first is storing data. So, in all the companies that I've worked at, all of the data sets were always stored on the cloud.
So, this is on S3 buckets on Amazon Web Services
or cloud storage on Google Cloud Platform.
And I believe the equivalent is Blob storage on Azure.
You can imagine all of the storage services as big hard drives,
with essentially unlimited capacities that you pay for per GigaByte that you use.
The second use case is running application.
So, if we have an ETL application or a data processing application, it would always be running on the cloud.
Because these applications were part of an automated pipeline,
which would be triggered based on data from an external source.
For example, if the user send data through, through their mobile app, or through the web.
As you can imagine, these are processes that you really don't want to be running manually,
Because it's just so repetitive.
The third use case is running models.
Depending on the size of your data set, training a model can take a couple of days.
And, you can imagine how it's not exactly feasible on your local laptop.
I mean, if you were to train a model on your local laptop, you'd have to make sure that the laptop is always awake,
and it's not plugged out of power.
So instead, why not just use a computer on the cloud?
Which is almost always on, and a lot more reliable than your local computer.
So, you can just stop the model training on that computer and check the progress periodically.
Instead of sitting glued to the laptop trying to monitor the progress.
Computers on the cloud are always available and ready for use.
Apart from downtime, every now and then.
But this can fluctuate anywhere from 7 hours a month to half a minute.
Depending on how much cash you're willing to spend in your application requirements.
Now that you've got an idea on what the cloud is for,
let me talk you through some of the learning resources,
that can help you upscale yourself when it comes to learning about cloud platforms,
and how to use them?
For learning Amazon Web Services, I would recommend doing the edX online course for AWS.
You can start off by doing the AWS cloud practitioner course.
You can do all these courses for free, but if you want the certificate, you'll need to pay 100 Dollars.
Which isn't worth it.
Not many people look at these when they hiring.
Along with the course, I recommend getting an AWS account and using their free tier services.
As a new user, you have access to 12 months of free tier membership,
which allows you to the most popular services on the AWS for free.
For example, you can use SageMaker for 250 hours and have 750 free hours on the Linux machine.
For Google Cloud Platform, Qwiklabs is the best.
I've used it as my current job to learn all about GCP.
This platform follows the ethics of learning by doing,
Which you all know is my absolute favorite.
For each lab, you'll get temporary access to the Google Cloud Platform, where you can apply what you learn.
It's truly an engaging way of learning.
There's also a skills challenge being run by Google Cloud, which gives you 30 day free access to learn Google Cloud Platform.
You've got the option of choosing from four different tracks, including an ML and AI track, which is amazing.
Learning Azure is similar to learning AWS. You can learn about the basics from their website, which has multiple training courses.
Then you can sign up for free account, which gives you access to the most popular services for 12 months.
You don't need to do courses on all three platforms. Just pick one and get started.
Because whatever you learn in that one course, it'll be easily transferable to the other cloud platforms.
You should focus on learning how to store files, how to configure and use computers on the cloud,
And how to use the dedicated AI platforms on the cloud service?
So this is SageMaker on AWS, Vertex AI on GCP, and Azure AI platform on Microsoft.
Hope these resources help you get started on your cloud journey.
If you are enjoyed the video, then be sure to smash the like button, and I'll see you next time.