🧠 MLOps Mastery: Seamless Multi-Cloud Pipeline with Amazon SageMaker and Azure DevOps 🌐

Author(s): DiPAK KNVDL

Originally published on Towards AI.

How modern enterprises build scalable, production-ready ML systems across AWS & Azure

In today’s data-driven world, enterprises generate massive amounts of data across distributed systems, mobile applications, IoT devices, and transactional platforms. Transforming this raw data into real business value requires a strategic pipeline for building, training, deploying, and monitoring 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, particularly the integration of Amazon SageMaker and Azure DevOps to enhance ML lifecycle management. It outlines a clear, real-world blueprint for constructing a fully automated, secure, and scalable multi-cloud MLOps pipeline that incorporates various components such as CI/CD orchestration, model training and deployment, governance, and security measures while emphasizing the benefits such as vendor neutrality and scaling flexibility.

Read the full blog for free on Medium.

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Published via Towards AI