Data Science Workstation / Laptop Questionnaire Data Science Workstation / Laptop QuestionnairePlease enable JavaScript in your browser to complete this form.Name *FirstLastEmail *Phone Number *County/Organization *1. I use Python programming language for Data Science tasks (Pytorch, Tensorflow, Pandas, sklearn, numpYes, scipYes, numba). YesNoIf Yes, include any additional tools you use or would like to use in the future2. I use RAPIDS GPU accelerated python modules cudf, cuml and cugraph with Apache DaskYesNo If Yes, include any additional RAPID GPU use cases you use or need3. I would like to know more about speeding up ETL AI ML using NVIDIA RAPIDSYesNo4. How large is your organization’s data infrastructure (Total storage needs/capacity)?MB (Mega)GB (Giga)TB (Tera)PB (Peta)5. How large are your working datasets (average size of data science datasets)?0 – 8 GB8 – 16 GB16 – 24 GB 24 – 48 GB48 – 96 GB+96 GB6. My primary Data Science tasks are: (Check all that apply)Analyze and understand the dataData Preparation / ETLBuild prototypes for machine learningImprove existing ML/DL modelsInferenceAdditional Comments7. The GPUs I utilize for Data Science tasks are in: (Check all that apply) on-prem serversCloudWorkstationsI don't use GPUs for data scienceAdditional Comments8. I work on these use cases: (Check all that apply) Forecasting / PredictionsRisk / Fraud Detection / Anomaly Detection Log Analysis / Security AlertingRecommender SystemsNLPAdditional Comments9. I already use NVIDIA Automatic Mixed Precision (AMP) training leveraging Volta RTX Tensor Cores for faster training.YesNoAdditional Comments10. I haven't used AMP (yet)YesNoAdditional Comments11. Will your equipment be subject to extreme conditions, i.e. low/high temperatures, water/sun exposure, resulting in the need for ruggedized equipment / laptops?YesNo11a. If this applies, please highlight unique requirements from the list below (Check all that apply)Consistently running in temperatures > 95 degrees Fahrenheit / 35 Celsius)Consistently running in temperatures < 32 degrees Fahrenheit / 0 Celsius) Consistently running in locations with greater than 90% humidityConsistently running in locations with less than 10% humidityConsistently subject to > 110 g shocks Consistently subject drops of greater than 14” / 35 cmConsistently subject drops of greater than 29” / 73 cmConsistently subject to extreme dust (manufacturing areas, paint areas, sand, etc.) Consistently subject to extreme spills / water exposureConsistently subject to extreme conditionsPlease describe the extreme conditions:12. Are your countries power requirements different from US standard?YesNoPlease describe you country's power requirements:13. Concerning data science, please discuss your development experience (e.g. Python, alternatives such as R, Stata, etc.) What tools do you use? Also, how familiar are you with the Linux operation system / what versions?14. What are your major data science projects, activities, and deliverables for the next six months? Do you have a schedule of events that need support?15. Use the section below to describe any other data science concerns or challenges you would like to address. Send