Orchestrating ML Pipelines: Scalability, Reliability, And Governance
Machine learning (ML) is transforming industries, offering solutions from predictive analytics to personalized experiences. But the journey from raw data to a deployed ML model is complex, involving numerous steps that, if not managed correctly, can become a tangled mess. This is where ML pipelines come in – structured workflows that automate and orchestrate the entire ML lifecycle, leading to more reliable, efficient, and scalable solutions. This comprehensive guide will delve into the world of ML pipelines, exploring their benefits, key components, practical examples, and best practices.
What is an ML Pipeline?
An ML pipeline is an automated workflow that chains together multiple steps required to build, train, and deploy a machine learning model. Think of it as an assembly line for ML, ...








