AI is the buzzword while ML is the underlying component... but when do we use ML? To solve problems that machines can find patterns without explicitly programming them to do so.
But do you have a team building an ML model? How far are they from the IT team? Do they know how to deploy and serve that? Testing? And sharing what they have done?
That's where a devops mindset comes in: reduce the batch size, continuous-everything and a culture of failure/experimentation are vital for your data team!
In the end, I will show how the workflow of a data scientist can be on the real life with a live demo!