AutoGluon NeurIPS 2022 Workshop Survey

Question Title

* 1. What data modalities do you use most often?

tabular (categorical/numerical)

image

text

audio

video

Other (please specify)

Question Title

* 2. How do you describe yourself?

Researcher

Software engineer

Data scientist/Business analyst

Solution architect

Other (please specify)

Question Title

* 3. What is the biggest blocker for you to adopt SOTA deep learning models for your problem of interest?

Get data/label data

Find the right model architecture

Train a deep model properly

Model deployment

Other (please specify)

Question Title

* 4. In terms of open-source AutoML toolkit, which do you prefer?

Directly using it out of box, write as few lines of code as possible

Write more code is ok, but expect more flexibility in customization

Other (please specify)

Question Title

* 5. Check any of the following if it is an important feature for an AutoML system to have for you to get value from it.

Automated exploratory data analysis

Large-scale Pretraining

Fast inference

Model compilation

Distribution shift detection & correction

Distributed training

Strong accuracy

Fairness

Differential Privacy

Fast install

Other (please specify)

Question Title

* 6. What operating system do you use most for machine learning need?

Linux

Windows

Mac

Other (please specify)

AutoGluon NeurIPS 2022 Workshop Survey

Question Title

* 1. What data modalities do you use most often?

Question Title

* 2. How do you describe yourself?

Question Title

* 3. What is the biggest blocker for you to adopt SOTA deep learning models for your problem of interest?

Question Title

* 4. In terms of open-source AutoML toolkit, which do you prefer?

Question Title

* 5. Check any of the following if it is an important feature for an AutoML system to have for you to get value from it.

Question Title

* 6. What operating system do you use most for machine learning need?

Question Title

* 7. How many years of coding experience do you have?

Question Title

* 8. What features would you like to see beyond we have introduced in today’s workshop?

Question Title

* 9. Do you have any feedback for us after the hands-on session?