Welcome to our short explainer video on automating the deployment of AWS Sagemaker models! In this tutorial, we'll guide you through the process of seamlessly deploying your machine learning models using AWS Sagemaker, one of the most powerful tools for data scientists and machine learning engineers.

AWS Sagemaker is a comprehensive service that lets you build, train, and deploy machine learning models at scale. In this video we focus on the deployment aspect and show you how to connect and automate your model deployment processes to make your workflows more efficient and reliable. This is an essential guide for anyone looking to get started with AWS Sagemaker or improve their existing Sagemaker skills.

Key topics covered in this tutorial:

Introduction to AWS Sagemaker: Learn the basics of Sagemaker and how it fits into the broader AWS ecosystem.
Sagemaker 101: Understand the fundamental concepts and functionalities of Sagemaker.
Deploy Model: Step-by-step instructions for deploying your trained machine learning models.
Automate Deployment: Techniques to automate your model deployment using Sagemaker and S3.
Orchestra: How to integrate Orchestra with AWS Sagemaker for streamlined machine learning operations.
For more detailed information and more information, check out these resources:

getdbt.com
docs.getdbt.com
georkestr.io
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