Welcome to our short explainer video on automating AWS Sagemaker model deployment! In this tutorial, we will walk 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 allows you to 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 getting started with AWS Sagemaker or looking to improve their existing Sagemaker skills.

Important topics 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 basic concepts and features of Sagemaker.
Deploy Model: Step-by-step guide to deploying your trained machine learning models.
Deployment Automation: Techniques to automate your model deployment with Sagemaker and S3.
Orchestra: How to integrate Orchestra with AWS Sagemaker for optimized machine learning operations.
For more detailed information and further learning, see these resources:

www.getdbt.com
docs.getdbt.com
getorchestra.io
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