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Ooh thanks for this question @Eric! I think there’s a lot of cool things happening in the MLOps space! For one, there are a ton more Central ML teams across enterprises. Here’s a great piece from our customer success lead who works with many centralized ML teams: https://towardsdatascience.com/the-death-of-central-ml-is-greatly-exaggerated-1f1626b3a8d4 How they operate, how does central ml platform look like - all of this is still growing a TON. There’s also a lot more tools than back in 2016. A lot of infra needed to do ML back then had to be built in house. I don’t think this is the case these days. There is also a wider range of models deployed - we see more NLP & CV use cases with the advent of deep learning. Tools to build, deploy, troubleshoot these types of models and data are of more need because of the growing use