The cloud migration process involves moving all or part of an organization’s data, apps, and services from on-premises data centres to a public or private cloud, where they are accessible on-demand over the Internet to authorized users. For most businesses considering cloud migration, the move is filled with promise and potential; scalability, flexibility, reliability, cost-effectiveness, improved performance and disaster recovery, and simpler, faster deployment.

Cloud migration does not mean simply lifting and shifting your applications to a cloud platform. Instead, it involves assessing the application’s architecture and check if it is compatible with the technology stack of the Cloud platform. Moving an application that has a ‘stateful’ architecture to the cloud will hardly benefit from the move as it will be difficult to deploy and despite the move, the application will not be able to scale. Hence, the first step towards cloud migration will involve defining the goals and objectives behind cloud migration and then assessing if the transfer to the cloud will be beneficial or not.

In this case study, we’ll explore how we migrated our machine learning infrastructure to a cloud. We’ll dive into the migration process that we followed. Also, I’ll be including the decisions that made before and during the migration to make this happen.

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Migrating MLFlow Server To Cloud: Part 1
1.45 GEEK