Global Big Data Conference

For any business, seamless deployment of ML models into production is the key to success of its live analytics use cases. In this article, we will learn about deploying ML models on AWS (Amazon Web Services) using MLflow and also look at different ways to productionize them. Subsequently, we will explore the same process on the two other popular platforms: Azure and GCP. An Identity and Access Management execution role defined that grants SageMaker access to the S3 buckets. Once the above steps are done with, here's how we proceed with the deployment process on AWS - Before any model can actually be deployed on SageMaker, Amazon workspace needs to be set up.