Compare MLlib and Mahout 
- Users report that Mahout excels in its "Quality of Support," with a score of 8.8, indicating that users find the support team responsive and helpful, while MLlib's support quality is rated lower at 7.3, suggesting potential delays or less effective assistance.
- Reviewers mention that MLlib offers a superior "Ease of Use" experience, scoring 8.8 compared to Mahout's 7.1, which indicates that users find MLlib more intuitive and user-friendly for machine learning tasks.
- G2 users highlight Mahout's strong "Product Direction" with a positive rating of 8.9, suggesting that users feel confident in the product's future development, whereas MLlib's score of 7.5 indicates some uncertainty among its users regarding ongoing improvements.
- Users on G2 report that Mahout has a more challenging "Ease of Setup" with a score of 7.6, while MLlib shines in this area with a score of 8.7, making it easier for users to get started with their machine learning projects.
- Reviewers say that both products have similar "Meets Requirements" ratings, with Mahout at 8.3 and MLlib at 8.5, indicating that both products generally fulfill user needs, but MLlib has a slight edge in this aspect.
- Users report that Mahout's "Training Data" capabilities are robust, but MLlib's integration with Apache Spark enhances its performance in handling large datasets, making it a preferred choice for users needing scalability in their machine learning applications.
MLlib now works on the new DataFrame API and thus is very easy to use.
It is rigid with some of the algorithms, specially with advanced one like neural network. For instance, you are unable to change activation functions of a neural network. You can either use Sigmoid for all the layers, or tanh which is not really making...
Apache mahout is used for scalable machine learning project implementation and development
The compute time is relatively slow when compared to other similar frameworks like Mlib and tenserflow. Also, there's no enterprise support as such and it's purely an open source framework with limited online knowledge repository.
MLlib now works on the new DataFrame API and thus is very easy to use.
Apache mahout is used for scalable machine learning project implementation and development
It is rigid with some of the algorithms, specially with advanced one like neural network. For instance, you are unable to change activation functions of a neural network. You can either use Sigmoid for all the layers, or tanh which is not really making...
The compute time is relatively slow when compared to other similar frameworks like Mlib and tenserflow. Also, there's no enterprise support as such and it's purely an open source framework with limited online knowledge repository.
