Productive machine learning workflows in a hybrid cloud MLops world

Recent years have seen tremendous advances in state of the art machine learning models in domains such as speech, text & images. These deep learning models are extremely demanding on computational resources & require a new set of tools and processes for companies to take advantage of. Success depends on the scale of experimentation — running more experiments equals better results & teams need to be able to iterate across training these large demanding models and do so in a reproducible fashion. Misha is a PM at Paperspace working on Gradient, which is a serverless platform that makes it simple and fast to run machine learning and deep learning workloads of any scale and complexity on any infrastructure. He will outline the workflow of productive machine learning organizations so they can decrease time to market in implementing AI-enabled solutions across the enterprise.
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