02. What's Ahead?
What's Ahead?
In this lesson, you're going to get familiar with what's meant by machine learning deployment . Then in the upcoming lessons, you will put these ideas to practice by using Amazon's SageMaker . SageMaker is just one method for deploying machine learning models.
Specifically in this lesson, we will look at answering the following questions:
-
What's the
machine learning workflow
?
-
How does
deployment
fit into the
machine learning workflow
?
-
What is
cloud computing
?
-
Why would we use
cloud computing
for
deploying
machine learning models?
-
Why isn't
deployment
a part of many machine learning curriculums?
-
What does it mean for a model to be
deployed
?
-
What are the
essential
characteristics associated with the code of
deployed models
?
- What are different cloud computing platforms we might use to deploy our machine learning models?
At the end of this lesson, you'll understand the broader idea of machine learning deployment . Then Sean will be guiding you through using SageMaker to deploy your own machine learning models. This is a lot to cover, but by the end you will have a general idea of all the concepts related to deploying machine learning models into real world production systems .