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. |
System Requirements
Version ↓
| # | 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.) |
License
Categories
Alternatives
Machine Learning
Massive Online Analysis TensorFlow Apache Spark Apache MXNet Apache SystemDS Eclipse Deeplearning4j MALLET mlpack OpenCV Orange PyTorch scikit-learn The Microsoft Cognitive Toolkit Torch Weka Yooreeka
Cloud Computing
Eureka Kubecost Pulumi IaC Infracost Terraform by HashiCorp Velero Apache Hadoop Apache Spark
Massive Online Analysis TensorFlow Apache Spark Apache MXNet Apache SystemDS Eclipse Deeplearning4j MALLET mlpack OpenCV Orange PyTorch scikit-learn The Microsoft Cognitive Toolkit Torch Weka Yooreeka
Cloud Computing
Eureka Kubecost Pulumi IaC Infracost Terraform by HashiCorp Velero Apache Hadoop Apache Spark
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.