Apache Spark Components – Spark MLib

Objective

            The Objective behind Apache Spark Components – Spark MLib creation is to make machine learning scalable and  easy.

Introduction

Apache Spark Components – Spark MLib is fast and scalable Machine Learning component in apache spark. It deals with high-quality algorithm. Spark MLib provides various tools for the developers to simplify the development in production. Spark MLib is designed mainly for large-scale learning settings which benefit from model parallelism.

 

Advantages of Apache Spark MLib

  • Apache Spark Components – Spark MLib is easy to deploy and does not require any pre-installation, if Hadoop 2 cluster is installed and running.
  • Spark MLib is known for its scalability, simplicity, and language compatibility it support to write applications in Java, Scala, and Python,it helps data scientists to solve iterative data problems faster.
  • Spark MLib is build on the top of Apache Spark which helps in developing the efficient large-scale machine learning algorithms, they are iterative in nature.
  • Apache Spark is an open source software, it leds to the rapid growth and adoption of Spark MLib.
  • Spark MLib provides ultimate performance and is 10 to 100 times faster than Hadoop and Apache Mahout.

Features of Spark MLib                                                                            

  • Its is known for its algorithmic optimizations, accurate predictions and efficient distributed learning.
  • MLib is integrated with various spark components. so it can make use of its libraries and operators for data cleaning.
  • Spark MLib provides a package called ml used to simplify the development and increases the performance.
  • It provides high-level API’s which help data scientists to create standard learning approach.
  • Spark MLib provides fast and distributed implementations of machine learning algorithms along with a number of low-level primitives.
  • MLib library has documentation which provide all the detailed describes all the supported utilities and methods for several spark machine learning.
  • Having a very active open source community and frequent event occurs to encourage community contributions and enhancements.

Spark MLlib Tools

     Spark MLlib provides the following tools like,

1. ML Algorithms

It is a core part of MLlib. It include some of the common learning algorithms like classification and clustering etc

2. Featurization

It includes feature like extraction, transformation, dimensionality reduction and selection.

3. Pipelines

It is used to provides tools for evaluating,constructing and tuning ML Pipelines.

4. Persistence

It helps in saving and loading algorithms, models and Pipelines.

5.Utilities                     

It used for linear algebra, statistics and data handling.

Spark MLlib Algorithms

Some of the popular algorithms and utilities of Spark MLlib are as fallows,

  • Basic Statistics
  • Regression
  • Classification
  • Recommendation System
  • Clustering
  • Dimensionality Reduction
  • Feature Extraction
  • Optimization

Spark MLib Use Cases

The common business use cases for the Spark Mlib are as fallows ,

  • Operational Optimization
  • Risk Assessment
  • Fraud Detection
  • Marketing optimization
  • Advertising Optimization
  • Security Monitoring
  • Customer Segmentation
  • Product Recommendations

Companies Using Apache Spark MLib                                              

  • 24/7 is a predictive analytics company, uses Spark MLib to captures customers interactions across various channels.
  • Huawei big data solution ,uses Spark MLib for frequent pattern mining.
  • Toyota, uses Spark MLib for categorizing and prioritizing its customers on social media interactions in real-time.
  • Netflix and Spotify, uses Spark MLib to update their recommendation systems every few seconds.

Conclusion

          From the above topic we can conclude that Spark MLib makes machine learning scalable and easy, because of its popular Algorithms and tools.

 

References

https://spark.apache.org/mllib/