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.

Machine learning applied, SageMaker 2.

So we're still exploring SageMaker features.

We left off in the train and tune phase of the SageMaker tooling as all part of a data and

machine learning pipeline.

Let's spend a little bit more time discussing training because training is sort of going to be

the bread and butter of a machine learning engineer's day-to-day rule. Training your machine learning

model. In the past, we might write our model in Keras, TensorFlow, PyTorch, train that model on local

host, maybe in a Docker container, using our system's GPU. Well, a benefit of training in SageMaker

is that your model will be part of the pipeline, part of the stack. It will be receiving data

downstream from what you've already built out in your pipeline using Data Rangler and Feature

store and all these things. And if you build out your model into SageMaker so that when you're training your model on SageMaker,

it's able to be deployed through SageMaker, then you don't have to take that extra step

...