🧠 Master MLOps: Seamless multi-cloud pipeline with Amazon SageMaker and Azure DevOps 🌐

Author(s): Deepak KNVDL

Originally published on Towards artificial intelligence.

How modern enterprises build scalable, production-ready machine learning systems across AWS and Azure

In today’s data-driven world, organizations generate massive amounts of data across distributed systems, mobile applications, IoT devices, and transaction platforms. Converting this raw data into real business value requires a strategic pipeline of building, training, deployment, and monitoring Machine learning (ml) models on a large scale. This is the place MLOps The system of applying DevOps principles to machine learning plays a fundamental role.

This article discusses the importance of multi-cloud MLOps Strategies, particularly Amazon SageMaker and Azure DevOps integration to enhance machine learning lifecycle management. It outlines a clear, realistic blueprint for creating a fully automated, secure, and scalable multi-cloud MLOps pipeline that includes various components such as CI/CD orchestration, model training, deployment, governance, and security measures with an emphasis on benefits such as vendor neutrality and scaling flexibility.

Read the full blog for free on Medium.

Posted via Towards artificial intelligence