Orchestrating ML: Pipeline Design For Real-World Impact
Machine learning (ML) is revolutionizing industries, but deploying successful ML models requires more than just building the model itself. A robust ML pipeline is essential for automating the entire process, from data ingestion to model deployment and monitoring. This post delves into the world of ML pipelines, exploring their importance, components, benefits, and how to build and manage them effectively. Whether you're a data scientist, ML engineer, or just curious about the technology, this guide will provide a comprehensive understanding of ML pipelines.
What is an ML Pipeline?
Definition and Purpose
An ML pipeline is a series of automated processes that take raw data, transform it into a suitable format, train an ML model, evaluate its performance, and then deploy it for making predict...