Welcome back to Machine Learning Guide. I'm your host, Tyler Rinelli.

MLG teaches the fundamentals of machine learning and artificial intelligence.

It covers intuition, models, math, languages, frameworks, and more.

Where your other machine learning resources provide the trees, I provide the forest.

Visual is the best primary learning modality, but audio is a great supplement during exercise commute and chores.

Consider MLG your syllabus with highly curated resources for each episode's details at OCdevel.com forward slash MLG.

I'm also starting a new podcast which could use your support. It's called

Lefnear's Life Hacks and teaches productivity focused tips and tricks, some which could prove

beneficial in your machine learning education journey. Find that at OCDevelle.com forward slash

LLH. Welcome back to Machine Learning Applied.

Today we're going to be talking about developing within the AWS environment.

In other words, using AWS as your local development environment.

Now before we get started, let's reflect back on a prior episode about Docker. I did an episode

where I said you can package up an environment and its dependencies and the project's source code

into a Docker container and deploy that Docker container to the cloud. What we do is we write a

Docker file, literally

called Docker file with a capital D. At the top of that Docker file, you specify the operating

system you're going to be using, and then within the Docker file you're going to specify any number

of operating system level packages you want to install, like FFMPEG or kudaku dN, and you might install some pip

packages. You can either directly inline the Docker file, say pip install X, Y, and Z, or you can

have a requirements. text file that gets copied into the Docker container, and then that thing

gets kicked off with a pip install of the requirements. text.

...