Data Science has a lot of work that's actually very tedious and most data scientists prefer to avoid that work. Most data engineers and developers who are involved in the Machine Learning process prefer to avoid this as well.
From training the same model multiple times using different features or hyperparameters to preventing over-fitting.

What do you do with work you prefer not to do? You automate it!
Throughout the industry, the smartest people at Microsoft, Google, Facebook and others have been trying to tackle this issue for a while already.
With the results we see now, it's safe to say AutoML is here to stay.

So, people tend to have a lot of questions:
When and why should you use it?
What options does this open for your data analytics team?
When and why should you avoid it?
How do you ensure it has the largest impact possible?
Can you still be compliant certain requirements?

We'll explore these questions while keeping our mind open to all solutions that exist in the wild.