Offline vs. Online ML Pipelines: The Key to Scaling AI

This article highlights the crucial difference between offline and online machine learning pipelines in building scalable AI systems. Offline pipelines handle batch processing, such as data collection, ETL, and model training, while online pipelines serve predictions in real-time or near real-time to users. The article stresses the importance of separating these pipelines and uses a feature pipeline for fine-tuning a summarization SLM as an example. It explains how to build a reproducible, trackable, and scalable dataset generation process using MLOps frameworks like ZenML. This process extracts data from MongoDB, processes it through various stages, and finally publishes it to Hugging Face. Understanding this separation is crucial for building robust, production-level AI systems.