--- title: MLlib Reviews meta_title: 'MLlib Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 14 reviews by the users' company size, role or industry to find out how MLlib works for a business like yours. aggregate_rating: rating_value: 4.1 review_count: 14 scale: '5' date_modified: '2026-06-21' parent_category: name: Artificial Intelligence url: https://www.g2.com/categories/artificial-intelligence --- # MLlib Reviews **Vendor:** The Apache Software Foundation **Category:** [Machine Learning Software](https://www.g2.com/categories/machine-learning) **Average Rating:** 4.1/5.0 **Total Reviews:** 14 ## About 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. ## MLlib Reviews ### 1. Apache Spark - MLib review **Rating:** 4.0/5.0 stars **Reviewed by:** Chetan S. | Data Analyst, Small-Business (50 or fewer emp.) **Reviewed Date:** October 10, 2020 **What do you like best about MLlib?** 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 about MLlib?** 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 MLlib:** 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 is MLlib solving and how is that benefiting you?** ETL and data extraction. Fast data accessing can be performed using the tools ### 2. MLlib review **Rating:** 4.0/5.0 stars **Reviewed by:** Mohini S. | Small-Business (50 or fewer emp.) **Reviewed Date:** October 10, 2020 **What do you like best about MLlib?** implementation of ML algorithms like regression, classification and modelling techniques can be done using the tool **What do you dislike about MLlib?** MLlib is not production ready, moreover Spark does not come out as a useful engine owing to its latency **What problems is MLlib solving and how is that benefiting you?** Data extraction from the database as well as implementing ML models for a required query ### 3. Great Software! **Rating:** 5.0/5.0 stars **Reviewed by:** Akshay K. | Data Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** October 09, 2020 **What do you like best about MLlib?** The interface and the workstation is to top notch. Easy to navigate and experiment with. **What do you dislike about MLlib?** Nothing at all. All are perfect and efficient enough. **Recommendations to others considering MLlib:** Highly recommended to all the ML geeks out there. **What problems is MLlib solving and how is that benefiting you?** Machine learning, Data Analysis and a lot of other technical aspects. ### 4. Effectiveness of Mlib **Rating:** 5.0/5.0 stars **Reviewed by:** Kunal B. | Senior Engineer - Data Engineering, Mid-Market (51-1000 emp.) **Reviewed Date:** August 28, 2020 **What do you like best about MLlib?** Distributed computing helps in speed and efficiency **What do you dislike about MLlib?** Nothing is bad, everything about Spark is great **Recommendations to others considering MLlib:** Must use for ML development. **What problems is MLlib solving and how is that benefiting you?** Distributing the workload over the cluster helps speed up the computation ### 5. Best scalable machine learning framework. **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Mid-Market (51-1000 emp.) **Reviewed Date:** December 10, 2019 **What do you like best about MLlib?** The scalability power of the framework which handles large data efficiently and performs machine learning algorithms at faster rate. **What do you dislike about MLlib?** The syntax and code changes for python R depends on the tools we are using.It is not standard which is tough for new users to adapt.The packages are very different compared tools to tool. **Recommendations to others considering MLlib:** If your problem is the large data to solve organization problems using machine learning then MIlb is the right one to use. **What problems is MLlib solving and how is that benefiting you?** We are solving the large data problems in our organization so that it would be salable and works faster for us. ### 6. ML Lib a Machine Learning library on Spark **Rating:** 4.0/5.0 stars **Reviewed by:** Dhawal G. | Undergraduate Reseacher , Mechatronics Instrumentation and Control Lab, Research, Small-Business (50 or fewer emp.) **Reviewed Date:** February 19, 2019 **What do you like best about MLlib?** MLLib was used as part of course in my college for Big Data. So we got to study why actually mllib came about and what all inadequacies were there in the Map-Reduce Framework of Hadoop and how apache Spark has solved them. The best part is the ease of use of Mllib and also the excellent documentation support from both the official website as well as the sources outside like youtube videos. The big community makes it easy to learn and use mllib. I used mllib for decision trees and I being a student was successfully able to implement the same with ease. Plus the python implementation is very easy to implement. **What do you dislike about MLlib?** We were given a preinstalled system for our labs and a cluster, but when I tried to do the same for my machine, I found it rather tricky to install. Also, support for deep learning is not there, which is a very fast growing field of machine learning. **Recommendations to others considering MLlib:** Good and easy to use library for multi cluster computing but only for conventional machine learning problems. Currently not adept with the deep learning support which may be nice in the future. **What problems is MLlib solving and how is that benefiting you?** I did a course on Big Data where I used Hadoop and apache spark to learn the various techniques used to deal with big data. Here I used MLlib to do a course project on classification, where I built a model a decision tree model from the data that I acquired by scraping humongous amount of sites. ### 7. A good library with futuristic short comings **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Telecommunications | Enterprise (> 1000 emp.) **Reviewed Date:** December 02, 2019 **What do you like best about MLlib?** MLib so far is the best community supported widely used machine learning library for apache spark **What do you dislike about MLlib?** MLib is inconsistent with deep learning models, this causes issues while moving models to production **Recommendations to others considering MLlib:** If you need to quickly move models to big data systems, MLlib is your answer **What problems is MLlib solving and how is that benefiting you?** Mostly we solve linear machine learning problems with MLlib ### 8. Useful tool for in-memory ML pipelines **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** December 02, 2019 **What do you like best about MLlib?** Speed and ease of use. Strong community support and lots of resources. **What do you dislike about MLlib?** Prototyping can be time consuming. Also, limited utility in case of extremely large datasets. **What problems is MLlib solving and how is that benefiting you?** Used MLlib for analyzing ads data for a large firm in order to suggest more topical ads. ### 9. MLlib is a convenient parallelized ML library **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** March 21, 2019 **What do you like best about MLlib?** I love how it includes most of the popular ML libraries for easy use with Apache Spark and parallelized computing. The power is only limited by the number of cores you've got. **What do you dislike about MLlib?** I feel like some other ML frameworks have better models, or added features/functionality used in developing models. MLlib is also an open source part of Spark, so development of the framework depends largely on what Open Source folks contribute to it. **What problems is MLlib solving and how is that benefiting you?** I'm doing ML problems with Apache Spark dataframes. The benefits are we can massively parallelize our training and modeling. I've worked with customers who used MLlib to build out random forest decision trees with massive tree depth and massive tree count. This would be impossible without MLlib. ### 10. Distributed ML in Spark with limited flexibility, especially for advanced algorithms **Rating:** 2.5/5.0 stars **Reviewed by:** Saeid A. | Data Scientist and Researcher, Outsourcing/Offshoring, Enterprise (> 1000 emp.) **Reviewed Date:** April 20, 2018 **What do you like best about MLlib?** It is distributed and allow distributed execution of model training ans well as model scoring. It helps to leverage benefit of Spark without using Scala. It delivers Spark ML with Python! High performance since it is a RDD-based data modeling package. Fairly nice documentation. **What do you dislike about MLlib?** 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 sense! Evaluation metrics are not as rich as packages like Scikit-Learn. Not all its functionalities implemented in Python. Many are Scala-based yet. **Recommendations to others considering MLlib:** If you bother about advanced algorithm in specific neural network, do not use MLlib as it does give you least flexibility in customizing the network. Perhaps it is great for regression and decision tree in distributed environment. **What problems is MLlib solving and how is that benefiting you?** MLlib has both classification and regression algorithms under supervised learning and also k-means under unsupervised learning. The beauty of the package lies in its distributed execution. ### 11. Trial usage but positive experience **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Financial Services | Enterprise (> 1000 emp.) **Reviewed Date:** June 26, 2018 **What do you like best about MLlib?** The best features of the program include increasing speeds of computations and has high quality algorithms. **What do you dislike about MLlib?** I dislike that we have not fully implemented the product so I am not fully informm **What problems is MLlib solving and how is that benefiting you?** The best features of the program include increasing speeds of computations and has high quality algorithms. ### 12. Full fledged ML library support for Spark **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Wireless | Mid-Market (51-1000 emp.) **Reviewed Date:** June 12, 2018 **What do you like best about MLlib?** Easy to follow if you have the basic understanding of the ML algorithms. Clear interface to tune in parameters for the algorithm **What do you dislike about MLlib?** I have not faced any issue as such with this library. **What problems is MLlib solving and how is that benefiting you?** I have used this for building a recommendation system based on the Alternating Least Squares algorithm. ### 13. MLlib is a great API in Spark, which is a great framework that is constantly evolving **Rating:** 4.5/5.0 stars **Reviewed by:** Scott E. | Principal Consultant, Data Science, Computer Software, Mid-Market (51-1000 emp.) **Reviewed Date:** July 31, 2017 **What do you like best about MLlib?** It's FAST, and you can access the API from Python, R, SQL, Scala, or Java -- so works great for data science teams that use multiple languages. **What do you dislike about MLlib?** Sometimes, a feature that is new is only available from one of the languages, but that is OK, they try and expand it to the other languages ASAP! **Recommendations to others considering MLlib:** try it! you won't be sorry **What problems is MLlib solving and how is that benefiting you?** Generating recommendations for an entertainment video site and apps. ### 14. analytics in finance **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Financial Services | Enterprise (> 1000 emp.) **Reviewed Date:** April 13, 2017 **What do you like best about MLlib?** MLlib now works on the new DataFrame API and thus is very easy to use. **What do you dislike about MLlib?** Some of the functionality is still not reachable from PySpark **What problems is MLlib solving and how is that benefiting you?** I am signaligh early signs of defauls on loan portfolios. - [View MLlib pricing details and edition comparison](https://www.g2.com/products/mllib/reviews?section=pricing&secure%5Bexpires_at%5D=2026-06-25+13%3A32%3A34+-0500&secure%5Bsession_id%5D=875fb3e2-8767-434c-97c8-7da865e57163&secure%5Btoken%5D=2743f1d0b606f2fbe4531353a13c6feaadea8658fc03b1867feb8f108196f519&format=llm_user) ## MLlib Features **Integration - Machine Learning** - Integration **Learning - Machine Learning** - Training Data - Actionable Insights - Algorithm ## Top MLlib Alternatives - [scikit-learn](https://www.g2.com/products/scikit-learn/reviews) - 4.8/5.0 (60 reviews) - [Automation Anywhere Agentic Process Automation](https://www.g2.com/products/automation-anywhere-agentic-process-automation/reviews) - 4.5/5.0 (4,040 reviews) - [Demandbase One](https://www.g2.com/products/demandbase-one/reviews) - 4.4/5.0 (1,942 reviews)