Orchestrating ML: Scalable Pipelines For Real-World Impact
Machine learning (ML) has revolutionized industries, transforming data into actionable insights. However, the journey from raw data to a deployed ML model is rarely a straight line. It's a complex process involving data preparation, model training, evaluation, and deployment. That's where ML pipelines come in – streamlining and automating this workflow, ensuring efficiency, reproducibility, and scalability. This post delves into the intricacies of ML pipelines, exploring their benefits, key components, and practical considerations.
What are ML Pipelines?
Definition and Purpose
An ML pipeline is a series of interconnected steps designed to automate the entire machine learning workflow, from data ingestion to model deployment. Think of it as an assembly line for ML models, automating repetit...