MLlib vs. Torch
A side-by-side comparison based on G2 review data.
| Metric | MLlib | Torch |
|---|---|---|
| Overall rating | 4.1 | 4.4 |
| Review count | 14 | 15 |
| Ease of use | 9.0 | 9.1 |
| Ease of setup | 8.9 | 8.4 |
| Quality of support | 7.7 | 8.4 |
| Meets requirements | 8.7 | 9.1 |
| NPS | 35.714286 | 78.571429 |
| Small Business | 21.4% | 40.0% |
| Mid-Market | 50.0% | 20.0% |
| Enterprise | 28.6% | 40.0% |
| Total page views | 22 | 81 |
| Total LLM citations | 6 | 106 |
MLlib
MLlib is Spark's machine learning (ML) library that make practical machine learning scalable and easy it provides ML Algorithms: common learning algorithms such as classification, regression, clustering, and collaborative filtering, feature extraction, transformation, dimensionality reduction, and selection, tools for constructing, evaluating, and tuning ML Pipelines, saving and load algorithms, models, and Pipelines and linear algebra, statistics, data handling, etc.
Torch
Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first.