Back to Basics: Using SageMaker Project Templates to Consistently Scale MLOps Enterprise Wide

Your video will begin in 10
75 Views
Published
In this episode, join Arnab as he walks through how an organization can build a Machine Learning Operations (MLOps) platform using Amazon SageMaker to accelerate iterative model development and deployment. With the use of this pattern, organizations can quickly empower their teams with a robust platform that fosters speed to delivery and doesn’t compromise your governance and security posture.  

Additional Resources:
Amazon SageMaker overview: https://docs.aws.amazon.com/sagemaker/latest/dg/whatis-features-alpha.html
Amazon SageMaker Pipelines: https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-sdk.html
SageMaker Projects: https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-projects.html
Lab: https://catalog.us-east-1.prod.workshops.aws/workshops/63069e26-921c-4ce1-9cc7-dd882ff62575/en-US/lab6

Check out more resources for architecting in the #AWS cloud:
http://amzn.to/3qXIsWN

#AWS #AmazonWebServices #CloudComputing #BacktoBasics #AIML #MachineLearning #MachineLearningModels #SageMaker #MLOps
Category
Amazon Web Services
Tags
AWS, Amazon Web Services, Cloud
Be the first to comment