ML on Hadoop: Tools & Methods Guide

Machine learning algorithms can be implemented on Hadoop using the following methods and tools:

1. Apache Mahout is an open-source machine learning library that can run on Hadoop. It offers classic machine learning algorithms such as clustering, classification, recommendation, etc., making it easy to perform distributed computing on large-scale datasets.
2. Apache Spark is a fast, general-purpose cluster computing system that can integrate with Hadoop. It offers a machine learning library called MLlib, which includes common algorithms like regression, classification, and clustering, allowing for distributed computing on Hadoop clusters.
3. H2O is an open-source machine learning and artificial intelligence platform that can run on Hadoop and Spark. It offers a range of high-performance machine learning algorithms that can easily perform distributed computing on large-scale data.
4. TensorFlow on Hadoop: TensorFlow is a popular deep learning framework that can be used for distributed computing on Hadoop clusters. By integrating TensorFlow with Hadoop, it is possible to train deep neural network models on large datasets.

In general, implementing machine learning algorithms on Hadoop requires consideration of distributed storage and computation of data, as well as selecting the appropriate tools and frameworks to achieve this. The mentioned tools and methods can all help in implementing machine learning algorithms on Hadoop.