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.
Chetan S.
Advanced user of MLlib
★★★★★
Apache Spark - MLib review
What do you like best?
It is useful in implementing machine learning algorithms like classification, regression and clustering. It works well while using statistical modelling techniques
What do you dislike?
It has an expensive memory with the necessity of manual optimization which might degrade user experience. It gives latency but can be used amongst R and python communities
Recommendations to others considering the product:
This can be preferred if the request is to extract and access the data quickly. Also certain algorithms work well with the tool based upon the distinct requirements. Budget is also a factor to be looked upon
What problems are you solving with the product? What benefits have you realized?
ETL and data extraction. Fast data accessing can be performed using the tools
Review source: G2.com
