Author's): DiPAK KNVDL
Originally published in Towards Artificial Intelligence.
How modern enterprises build scalable, production-ready ML systems on AWS and Azure
In today's data-driven world, enterprises generate vast amounts of data across distributed systems, mobile applications, IoT devices, and transaction platforms. Transforming this raw data into real business value requires a strategic pipeline to build, train, deploy, and monitor machine learning (ML) models at scale. This is where MLOps — the discipline of applying DevOps principles to machine learning — plays an essential role.
This article discusses the importance of multi-cloud MLOps strategies, specifically integrating Amazon SageMaker and Azure DevOps to improve ML lifecycle management. It presents a clear, real-world roadmap for building a fully automated, secure, and scalable multi-cloud MLOps pipeline that covers various components such as CI/CD orchestration, model training and deployment, governance, and security measures, while highlighting benefits such as vendor neutrality and scaling flexibility.
Read the entire blog for free on Medium.
Published via Towards AI
