What are your Machine Learning Challenges?
Seeking to understand the most difficult aspects of in-house ML roles
We are helping businesses better understand the struggles and pain points of the modern ML team.
This survey takes an average of 4 minutes.
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1.
What is your title?
(Required.)
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2.
Do you manage a team?
(Required.)
Yes
No
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3.
Do any of the following represent a problem in your organization?
(Required.)
No issues
Minor frustration
Significant frustration
Major headache
Business critical issues
Gather Unlabeled data
No issues
Minor frustration
Significant frustration
Major headache
Business critical issues
Gather Labeled Training Data
No issues
Minor frustration
Significant frustration
Major headache
Business critical issues
Managing your data warehouse/pipeline
No issues
Minor frustration
Significant frustration
Major headache
Business critical issues
Building/training models
No issues
Minor frustration
Significant frustration
Major headache
Business critical issues
Using models in production
No issues
Minor frustration
Significant frustration
Major headache
Business critical issues
Updating models with new data
No issues
Minor frustration
Significant frustration
Major headache
Business critical issues
Machine learning project management
No issues
Minor frustration
Significant frustration
Major headache
Business critical issues
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4.
What are the most pressing technology problems facing ML efforts in your company? The more specific the better.
(Required.)
5.
Where do you source labeled training data?
Public data sources (e.g. ImageNet, BooksCorpus)
Previous user interactions in your product
Manually labeled data using a labeling service (e.g. Amazon Mechanical Turk)
Manually labeled data using internal resources
Bootstrapping high confidence labels from your model
Other (please specify)
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6.
What is the most difficult/frustrating aspect of sourcing labeled training data?
(Required.)
7.
Are you investigating or using third-party services to help with sourced labeling?
Yes
No
8.
If yes, what are you investigating or using? If no, why not?
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9.
How do you see Machine
learning changing at your organization?
(Required.)
Growing a lot
Growing a little
Staying the same
Shrinking a little
Shrinking a lot
10.
We are interested in building products to help you in this space! If you would be interested in a follow-up conversation, please leave your email or phone number below.
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