Hello everyone. Welcome to an Introduction to AWS SageMaker. In this course, we're going to take an introductory look at using SageMaker platform, specifically within the context of preparing data, building and deploying machine learning models.

Over this course, you'll gain a practical understanding of the steps required to build and deploy these models along with learning how SageMaker can simplify this process by handling a lot of the heavy lifting both on the model management side, data manipulation side, and other general quality of life tools.

There's a lot of different job roles and levels of experience that would benefit from SageMaker. So the prerequisites here are pretty broad. Generally speaking, people who will find SageMaker the most useful are those with some data or developer background, either if you're a data scientist, a data engineer, a SQL developer, or maybe you're more of a Python programmer who needs a better way to run their code. Ideally, as well, you have some experience with data and machine learning concepts. And in general, a familiarity with Amazon's cloud services will really help you understand it. That being said, SageMaker is one of these programs that's easy to learn, difficult to master.

So if you find yourself here at the relatively beginner level, don't fret, this is a great place to start your machine learning and data engineering journey. Especially because there's a ton of labs in the Cloud Academy library that use SageMaker as a central focal point to actually get the lesson and provide hands-on experience.

With such a broad set of people who can attend this class, really anybody interested in data, data science, data engineering, or frankly just wants to better understand how to run Python and machine learning on the AWS platform would really benefit from understanding how SageMaker works.

Before we kick off just a little bit about me, my name is Chris Gambino. I'm one of the lead architects and founders of Calculated Systems. Before joining up with Calculated Systems, I was a big data specialist at Google and Hortonworks, in which I helped people onboard their solutions onto the cloud and integrate with complex technologies such as Hadoop. Across these companies, I've had the chance to work with SageMaker notebooks and frankly IPython notebooks, which are now rebranded Jupyter notebooks, which are now rolled into SageMaker. So I've been working with this technology for a while. And personally, this is the technology that I use in use cases, such as when we're developing machine learning algorithms for streaming hundreds of millions of events per day to determine sentiment on social media posts.

Now, SageMaker isn't necessarily used in every point of that process. But honestly, in my personal opinion, this is one of the best tools for starting to get a grip on your data and building out your models. There's other ways to execute and use the models for inferencing. But I really do mean when I say SageMaker is a phenomenal tool and the tool that I personally use when starting to manipulate data on Amazon.