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 Ocdevel.com forward slash

LLH. Welcome back to machine learning applied and this is going to be a very important episode

where we discuss Amazon SageMaker, AWS Amazon Web Services, SageMaker.

In the last episode, I talked about deploying your machine learning model to the server.

It was an episode called Machine Learning Server. Well,

I was in over my head. I'm older and wiser now, and I know now that this concept is called

machine learning operations, or MLOPS. Machine learning operations. You may be familiar if you're

a web developer or a server developer with something called DevOps, developer operations,

which is effectively replaced systems administration, is the concept of deploying your server to the

cloud, your front end to the cloud, etc., making these servers scalable, microservices architectures,

all these things. Well, deploying your machine learning models to the server,

a new concept in the world of data science and machine learning is called MLOps or machine

learning operations. In the last episode, the machine learning server episode, I talked about a few

services. I did talk about SageMaker. I talked about AWS Lambda. And then I talked about a handful of auxiliary

...