MLlib vs. Dataiku
A side-by-side comparison based on G2 review data.
| Metric | MLlib | Dataiku |
|---|---|---|
| Overall rating | 4.1 | 4.4 |
| Review count | 14 | 267 |
| Ease of use | 9.0 | 8.8 |
| Ease of setup | 8.9 | 8.4 |
| Quality of support | 7.7 | 8.7 |
| Meets requirements | 8.7 | 8.9 |
| NPS | 35.714286 | 58.267717 |
| Small Business | 21.4% | 18.6% |
| Mid-Market | 50.0% | 22.4% |
| Enterprise | 28.6% | 58.9% |
| Total page views | 22 | 1,763 |
| Total LLM citations | 6 | 16,600 |
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.
Dataiku
Dataiku is the Platform for AI Success: the AI orchestration layer where enterprises build, deploy, and govern analytics, models, and agents at scale. It sits on top of the data platforms, clouds, and AI services you already use, working across all of them without locking you into any one. Dataiku expands who can build production AI, putting the right tools in the hands of data scientists and domain experts alike, from fraud analysts to demand planners. It orchestrates machine learning, rules, LLMs, and agents as one governed system, built on more than a decade of running production AI.
- Features
- Ease of Use
- Usability
- Easy Integrations
- Productivity Improvement
- Collaboration