How to Build a Machine Learning Training and Deployment Pipeline

Lightning is presented as an MLOps platform that extends PyTorch Lightning from distributed model training to deployment, serving, monitoring, and data engineering, aiming to reduce the complexity of fragmented machine learning workflows. Built around the open-source Lightning App framework, it allows Python applications to run locally, on Lightning Cloud infrastructure backed by AWS, or on private clusters with minimal code changes. Its core components, LightningWork and LightningFlow, support the construction and orchestration of long-running tasks such as training, inference, ETL, and application demos, while enabling developers to specify compute resources including CPUs, GPUs, memory, and storage through Python APIs. The example architecture uses a Flow to coordinate separate training and deployment Works, allowing each task to use different infrastructure, such as GPUs for training and CPUs for serving. The platform can be launched locally or in the cloud through command-line options and can be expanded with production capabilities including autoscaling, dynamic batching, hyperparameter optimization, drift detection, and model monitoring.

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