[D] Good strategies / resources to improve MLOps skills as a PhD student / researcher

A lot of researchers / PhD students in ML have prospects of joining the industry eventually (in US about 80% of ML PhDs are in the industry, according to the recently released Stanford's AI Index).

What are some good tips / resources for someone to ensure he develops more practical & deployment-oriented MLOps skills?

For example - setting up clusters, relevant cloud services (e.g. AWS), Docker, Kubernetes, developing internal tools for model training / data labelling... Stuff like that.

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