Snorkel Flow vs. NLP Cloud
A side-by-side comparison based on G2 review data.
| Metric | Snorkel Flow | NLP Cloud |
|---|---|---|
| Overall rating | 4.3 | 4.3 |
| Review count | 17 | 13 |
| Ease of use | 9.0 | 9.0 |
| Ease of setup | 9.3 | 9.0 |
| Quality of support | 8.6 | 8.3 |
| Meets requirements | 9.1 | 8.7 |
| NPS | 47.058824 | 53.846154 |
| Small Business | 41.2% | 84.6% |
| Mid-Market | 52.9% | 7.7% |
| Enterprise | 5.9% | 7.7% |
| Total page views | 64 | 127 |
| Total LLM citations | 139 | 87 |
Snorkel Flow
Modern AI approaches require massive labeled training datasets to learn from, which traditionally rely on armies of human annotators to label by hand. In Snorkel Flow, users programmatically label, build, and augment training data to drive a radically faster, more flexible, and higher quality end-to-end AI development and deployment process
- Flexibility
- Data Management
- Easy Integrations
NLP Cloud
NLP Cloud serves high performance NLP models from Hugging Face and spaCy. You can perform NER, sentiment-analysis, classification, summarization, text generation (with GPT-J and GPT-Neo, the open-source versions of GPT-3), question answering, and POS tagging. It's ready for production, and served through a REST API. You can also deploy your own Hugging Face transformers-based models and spaCy models.