Apache Mahout
A framework for creating scalable machine learning algorithms, designed to handle big data processing across distributed computing environments
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| + | Scalability | Works well in distributed environments using Hadoop |
|---|---|---|
| + | Cloud Compatibility | Scales effectively in the cloud with Apache Hadoop library |
| + | Performance | Enables quick analysis of large data sets |
| + | Clustering Algorithms | Includes k-means, fuzzy k-means, Canopy, Dirichlet, and Mean-Shift |
| + | Classification | Supports Distributed Naive Bayes and Complementary Naive Bayes |
| + | Evolutionary Programming | Offers distributed fitness function capabilities |
| + | Matrix and Vector Libraries | Contains libraries for mathematical operations |
| + | Recommendation Techniques | Implements Alternating Least Squares and Co-Occurrence algorithms, utilized by companies for recommendation systems |
| + | Expressive Scala DSL | Allows quick implementation of algorithms |
| + | Multiple Backend Support | Compatible with various distributed backends, including Apache Spark |
| + | Modular Native Solvers | Provides solvers for CPU/GPU/CUDA acceleration |
| - | Computing time | Slower computing time compared to other frameworks like MLlib and TensorFlow. |
| - | Unsupported algorithms | Removal of unsupported algorithms planned for future releases due to optimization issues with some algorithms in earlier versions. |
| - | Hadoop’s limitations | Hadoop’s limitations with highly iterative processes, affect Mahout’s performance. |
| - | Intermediate Caching | No caching of intermediate results across steps in long computations with Hadoop. |
| - | Data types and Hashing | Limited support for primitive types and open hashing in Mahout Collections. |
...11 more features. Contact us to get a complete list of features and system requirements.
System Requirements
| # | Minimum |
|---|---|
| 1 | Java 1.6.x or greater |
| 2 | Maven 3.x to build the source code |
| 3 | If implemented to work on Apache Hadoop clusters, Hadoop 0.20.0 or greater |
| 4 | CPU, Disk and Memory requirements are based on the many choices made in implementing your application with Mahout (document size, number of documents, and number of hits retrieved to name a few.) |
Ratings
5.005
| G2CROWD | 5.05 based on 1 reviews |
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License
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Notes
- Apache, Apache Mahout name and logo are trademarks of Apache Software Foundation.
- A mahout is a word used in South Asian countries to describe one who drives an elephant as its master. The name comes from its close association with Apache Hadoop which uses an elephant as its logo. Many of the implementations use the Apache Hadoop platform.
Update 2026:
- Apache Mahout until around 2024 focused on compute back-ends such as Spark and Flink for processing training data into predictions. More recently the project has adopted quantum compute back-ends. The QuMat library is a Python-based interface to multiple quantum computing systems, starting with IBM’s Qiskit, which allows researchers and developers to assemble quantum logic gates into circuits that can run on simulators as well as utility-scale quantum computers. More information in this talk, and in this PDF.