Know Why You Should Use Spark For Machine Learning


As business organizations are building more diverse and user-centric data products and services, the demand for machine learning is growing rapidly for predictive insights, personalization, and recommendations. Earlier, data scientists were able to solve these problems using popular tools such as Python and R. But as companies are producing and amassing a large amount of data, data scientists are spending a major portion of their time supporting their data infrastructure rather than creating the models to solve data problems. 

To help in solving this problem, Apache Spark offers a general machine learning library known as MLib, which is exclusively designed for simplicity, scalability, and quick integration with other tools. With the scalability, speed and language compatibility of Apache Spark, data scientists can solve and iterate through their data problems easier and faster. Undoubtedly, MLlib’s adoption is growing very quickly as can be seen through the large number of developer contributions and the expanding diversity of use cases.

Know How Spark Enhances Machine Learning

Today, R and Python have become the most popular programming languages owing to the large number of library modules that are readily available to help data scientists solve their data related problems.  But traditional uses of these tools were very limited, since they process large amount of data on a single machine, where the movement of voluminous data becomes time-consuming.  Meticulous analysis requires sampling and moving from development to production settings, entailing comprehensive re-engineering.

To overcome these problems, Apache Spark offers data scientists and data engineers a robust and unified processing engine that is easy and super-fast to use (up to 100x faster than Apache Hadoop for large-scale data processing). This allows data engineers to solve their machine learning problems interactively and with greater scalability.  Furthermore, Spark provides multiple programming language choices, including R, Python, Java, and Scala. The Spark Survey 2015 conducted by data bricks, that polled the Spark community, clearly reflects the rapid growth in Python and R. The survey report shows that 58% of respondents were using Python, while 18% of respondents were already using the R API.
 
The importance of machine learning has also not been overlooked, with 44% of respondents of the 2015 Spark Survey using Spark for creating recommendation systems and 64% of respondents using Spark for advanced analytics.
If you are interested in knowing more about Machine Learning with Spark, we suggest you enroll for a machine learning program from any good online training institute.

Location: Bengaluru, Karnataka, India

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