Distributed Machine Learning Is The Answer To Scalability And Computation Requirements

We all know the traditional way of machine learning, where programmers use an integrated tool for data mining and conduct analysis on the results. However, the traditional way may not work if the data is too large to store in the RAM of a single computer. Most existing ML algorithms are designed by assuming that data can be easily accessed, which means the same data may be accessed many times.

It was this challenge to handle large-scale data due to scalability and efficiency of learning algorithms with respect to computational and memory resources that gave rise to distributed ML. For example, if the computational complexity of the algorithm outpaces the main memory then the algorithm will not scale well and will not be able to process the training data set or will not run due to memory restrictions. Distributed ML algorithms rose to handle very large data sets and develop efficient and scalable algorithms with regard to accuracy and to requirements of computation (memory, time and communication needs).

Distributed ML algorithms are part of large-scale learning which has received considerable attention over the last few years, thanks to its ability to allocate learning process onto several workstations — distributed computing to scale up learning algorithms. It is these advances which make ML tasks on big data scalable, flexible and efficient.There are two approaches to distributed learning algorithms. The distributed nature of these datasets can lead to the two most common types of data fragmentation:

Some of the most common scenarios are where distributed ML algorithms are deployed are in healthcare or advertising where a simple application can accumulate a lot of data. Since data is huge, programmers frequently re-train data so as not to interrupt the workflow and use parallel loading. For example, MapReduce was built to allow automatic parallelisation and distribution of large-scale special-purpose computations that process large amounts of raw data, such as crawled documents or web request logs and compute various kinds of derived data.Several Distributed ML Platforms Are New

One of the most widely-used distributed data processing systems for ML workloads is Apache Spark MLlib and Apache Mahout. Microsoft also released its Distributed ML Toolkit (DMTK), which contains both algorithmic and system innovations.