Image by author using vqgan+clip (“underwater roboic world | trending on artstation”, 1000 iter.) Introduction In the past few years, machine learning data management practices have evolved dramatically, with the introduction of new design patterns and tools such as feature stores, data and model monitoring practices, and feature generation frameworks.

What led me here? A couple of years ago, I decided I needed a change. I was wrapping up 2.5 years as a VP of R&D at a medical imaging startup, building deep-learning-based solutions for the radiology domain.

Photo by Andrey Sharpilo on Unsplash Deep learning is notorious for consuming large amounts of GPU resources during training. However, there are multiple parts within the Deep Learning workflow that require large amounts of CPU resources :