Grid, which runs on AWS, supports Lightning and classic machine learning frameworks such as TensorFlow, Keras, PyTorch, Sci-Kit, and others. It also helps users to scale the training of models from the NGC catalogue. NGC catalogue is a curated set of GPU-optimised containers for deep learning, visualisation, and high-performance computing (HPC). PyTorch lightning software and developer environment is available on NGC Catalog. Also, check out GitHub to get started with Grid, NGC, PyTorch Lightning here.

The process of taking a machine learning (ML) experiment from a laptop or data science lab to production is not one that many people have experience with. Data Scientists are frequently charged with this daunting task since they understand the machine learning algorithm and likely proposed it in the first place. This blog describes how to start a successful production ML operational lifecycle (MLOps), by moving from a promising ML experiment to a Minimum Viable Product (MVP) of the same algorithm in a production service. MVPs are common in product development since they help get a product/service to a customer quickly, with just enough features to make it viable and drive usage-based feedback for the next version. In the ML context, MVPs help to isolate the critical needs of the production ML service and help to deliver it with the smallest possible effort.