--- 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: '2026-08-07' 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. 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. ### 4. 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. ### 5. 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 ### 6. 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. ### 7. 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. - [View MLlib pricing details and edition comparison](https://www.g2.com/products/mllib/reviews?filters%5Bnps_score%5D%5B%5D=4§ion=pricing&secure%5Bexpires_at%5D=2026-08-13+17%3A50%3A32+-0500&secure%5Bsession_id%5D=880a0a68-238b-4d44-a6a3-40c572b36ebf&secure%5Btoken%5D=8fb83656a3db31403a8d671270608d0ea06019b20c980ce47232687d6b515b14&format=llm_user) ## MLlib Features **Additional Functionality** - Tagging - Natural Language Processing - Data Extraction - Multi-Language - Predictive Analytics - Drag & Drop - Speech Recognition - Reporting/Analytics - Data Storage Management - Virtual Personal Assistant (VPA) - AI Copilot - Customer Segmentation - Collaboration Tools - Data Import/Export - Generative AI - For eCommerce - Role-Based Permissions - Customizable Branding - Search/Filter - Monitoring - Document Management - API - Data Visualization - Trend Analysis - Machine Learning - Access Controls/Permissions - Alerts/Escalation - Performance Metrics - Real-Time Data - Third-Party Integrations - Mobile App - Multiple Data Sources - For Sales Teams/Organizations - Sentiment Analysis - Activity Dashboard - Chatbot - Workflow Automation **Integration - Machine Learning** - Integration - Third-Party Integrations **Learning - Machine Learning** - Training Data - Actionable Insights - Algorithm **Additional Functionality** - Predictive Modeling - Configurable Workflow - Tagging - Data Import/Export - API - Predictive Analytics - Data Visualization - Endpoint Management - Multiple Data Sources - No-Code - Data Preparation - Auditing - Collaboration Tools - Big Data Analytics - ML Algorithm Library - Data Management - Activity Dashboard - Data Capture and Transfer - Activity Tracking - Data Connectors - Data Security - Data Extraction - Reporting & Statistics - Workflow Management - AI Copilot ## 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 (4,065 reviews) - [Demandbase One](https://www.g2.com/products/demandbase-one/reviews) - 4.4/5.0 (1,941 reviews)