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

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

Видео Back to Basics: Using SageMaker Project Templates to Consistently Scale MLOps Enterprise Wide канала Amazon Web Services