머신러닝 훈련 및 배포 파이프라인을 만드는 방법

Lightning is a platform from the creators of PyTorch Lightning that extends streamlined model training into serving, deployment, monitoring, and data engineering for production machine learning workflows. Built around the open-source Lightning App Framework, it lets users run Python applications locally, on AWS-based Lightning Cloud, or on private clusters with minimal infrastructure management, while allowing configurable compute resources such as CPUs, GPUs, RAM, storage, and software dependencies. Its architecture uses LightningWork components for long-running tasks including training, inference serving, ETL, and interactive demos, while LightningFlow coordinates multiple Works and defines application logic, such as training a model before deploying it. The platform supports separating hardware choices across pipeline stages, enabling models to train on GPUs and serve on CPU machines, and applications can be launched locally or in the cloud through simple command-line options. As machine learning systems grow, teams can add capabilities such as drift detection, monitoring, autoscaling, dynamic batching, and hyperparameter optimization to create more complete MLOps pipelines.

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