The Conference for Machine Learning Innovation

AutoML, the Good, the Bad and the Why

Session
Join the ML Revolution!
Register until April 30:
✓ Raspberry Pi or C64 Mini for free
✓Save up to 313 €
✓10 % Team Discount
Register Now
Join the ML Revolution!
Register until April 30:
✓ Raspberry Pi or C64 Mini for free
✓Save up to 313 €
✓10 % Team Discount
Register Now
Join the ML Revolution!
Register until May 28:
✓ ML Intro Day for free
✓ Raspberry Pi or C64 Mini for free
✓ Save up to $580
Register Now
Join the ML Revolution!
Register until May 28:
✓ ML Intro Day for free
✓ Raspberry Pi or C64 Mini for free
✓ Save up to $580
Register Now
Join the ML Revolution!
Register until November 7th:
✓Save up to € 210
✓10% Team Discount
Register Now
Join the ML Revolution!
Register until November 7th:
✓Save up to € 210
✓10% Team Discount
Register Now

Data Science has a lot of work that’s actually very tedious and most data scientists prefer to avoid that work.
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.

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This Session belongs to the Diese Session gehört zum Programm vom MunichMunich program. Take me to the program of . Hier geht es zum Programm von Singapore Singapore .

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Behind the Tracks