We’re excited to bring together Data Scientists and IT Decision Makers (ITDMs) to explore the landscape of model hubs and compute systems. Our focus is on understanding the preferred model hubs that Data Scientists use for accessing pretrained models, as well as the compute systems they rely on for AI development and creation. As our research progresses, we’re eager to share some preliminary insights with you. Here’s an update on our early findings:
Model Hubs (DS only)
Data scientists most commonly use platforms such as Microsoft AI GitHub, Google Model Garden, AWS Model Zoo, and PyTorch Hub.
| Hugging Face | 25% |
| Tensorflow Hub | 19% |
| Pytorch Hub | 31% |
| Microsoft AI GitHub | 38% |
| Model Zoo by OpenAI | 6% |
| ONNX Model Zoo | 6% |
| NVIDIA NGC | 6% |
| Google Model Garden | 38% |
| AWS Model Zoo | 31% |
| TorchVision Model Zoo | 13% |
| Qualcom AI Hub | 6% |
| Model Zoo for Intel Architecture | 0% |
| Determined AI Hub | 0% |
| NeuroPilot Model Hub | 13% |
| Other; specify | 0% |
| I don’t use any model hubs | 25% |
AI Systems
Data Scientists and ITDMs often use Azure NDv4, AWS EC2, Google CSP - TPU, and IBM Cloud when developing AI systems. Some also note that these systems are relatively new to their workflows and are actively working to familiarize themselves with these tools.
| AWS EC2 | 38% |
| Azure NDv4 | 44% |
| Google CSP (including Colab or Pods) – TPU | 24% |
| Google CSP (including Colab or Pods) – GPU | 18% |
| IBM Cloud | 24% |
| Oracle Cloud (OCI) | 18% |
| NVIDIA NIM | 20% |
| IBM Power Systems | 9% |
| Dell EMC for AI | 11% |
| HP Enterprises (HPE) Apollo Systems | 7% |
| Private Server(s) | 13% |
| On-prem Server(s) | 11% |
| On-prem workstations with NVIDIA GPU | 9% |
| On-prem workstations with AMD Radeon GPU | 7% |
| Other; specify | 2% |
| I don’t use any compute systems to create or develop AI | 7% |