ML+X Community Member Survey 
Please fill out this brief survey so that we can better understand the backgrounds, practices, and needs of machine learning (ML) practitioners working in Madison. The results from this survey will assist the ML+X community in planning future events.

This survey takes 5 to 10 minutes to complete and consists of four sections: ML-Related Work, Workshops, Community, and (optional) Contact Information
ML-Related Work
In this section we ask you a few questions about your ML-related work
What ML models/methods are you currently learning or using for your work?
How often do you use these programming languages in your ML-related work?
Never
Rarely
Sometimes
Often
Matlab
Python
R
Julia
Bash
C/C++
JavaScript
Java
Scala
Rust
Clear selection
Are there any other languages you sometimes or often use in your ML-related work?
Clear selection
How often do you use the following tools/frameworks in your ML-related work?
Never
Rarely
Sometimes
Often
HuggingFace
NVIDIA Tools
PyTorch
Keras
Tensorflow
Scikit-Learn (Sklearn)
XGBoost
Tidy Models (R)
Center for High Throughput Computing (CHTC)
Amazon Web Services (AWS)
GitHub Copilot
Google Cloud Platform (GCP)
Google Colab
ChatGPT
Microsoft Azure
Clear selection
If applicable, what platform(s) do you use to access/finetune pretrained models?
If applicable, what platform do you use to share trained models with others?
How often do the following challenges impede your ML-related work? 

Never
Rarely
Sometimes
Often
Difficulty knowing where to begin
Understanding the data prior to modeling (EDA pipelines)
Diagnosing and improving an ML model
Insufficient computing capabilities (memory, storage space, lack of GPUs, etc.)
Time and efficiency in staying up-to-date on latest methods
Library changes / inconsistencies
Insufficient quantity or quality of data
Lack of model explainability or interpretability
Ensuring fairness and appropriate model/data bias
Regulatory concerns (compliance with laws around protected data, auditing, FERPA, HIPAA, IRB, etc.)
Safety concerns and responsible use
Communicating results with stakeholders
Clear selection
Are there other challenges you sometimes or often face in your ML-related work?
In your ML-related work, how important are the following concerns to you?
Not important
A little important
Important
Very important
The model can be run on limited hardware
The model is well-documented (model use cases, training data sources, model cards, etc.)
The model is accurate
The model leads to good impacts
The model is fair
The model is not a black-box
The model's results are interpretable by end-users
Clear selection
Are their other things important to you in your ML-related work?
Next
Clear form
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