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 LLC.

You're listening to Machine Learning Applied, and in this episode,

we're going to talk about TechStack. Now, I know I've talked TechStack a lot in the past,

but we're going to get a little bit more specific at this time, and we're going to cover a broader

spectrum. We're going to talk about client, mobile, server, database, server, job server being your machine learning server.

And I'm going to make very specific technology recommendations in this episode.

And it's intended for people who really don't have an opinion, maybe one way or another,

or aren't using some specific web front-end framework or cloud hosting provider. If you have your tried and true tech

stack and you like what you like and you're using what you're using, you can go ahead and skip

this episode. But if you'd like some recommendations on where to start, building out a machine

learning customer-facing product, then this will be a good episode for you. So this episode assumes that we're talking about

a customer-facing machine learning product. If you're going to be developing machine learning

for a research project where you have a stakeholder who wants to know the predictions based on

...