Deliver Business Value Faster with Spark Machine Learning

Apache Spark Machine Learning (Spark ML) was introduced in Spark Version 1.4 and serves as a comprehensive solution for Machine Learning based on the Apache Spark computation engine. Spark ML introduces automation for the Machine Learning process via the Machine Learning Pipeline (ML Pipeline). Spark ML is based on the Apache Spark platform meaning it is built to scale and is fully capable of handling wide variety of data processing workloads.

mlpipeline

Forbes reports that on average, Data Scientists spend 60% of their time on cleaning and organizing data. Manual data cleansing is widely considered a tedious and expensive process, so it’s not surprising that 57% of Data Scientists consider this the least enjoyable part of their job.

The good news is that Spark ML includes many commonly used transformation functions directly out of the box. With these features included, data cleansing and processing becomes much more efficient. Prime examples of this are the common text analysis data preparations tasks of removing stop words and calculating term frequency (TF-IDF). Spark ML includes StopWordsRemover and HashingTF transformers and the IDF estimator allowing Data Scientists to implement their text analysis data preparation with just a few lines of code.

Now, Data Scientists can spend more time analyzing the data, and less time cleaning and transforming it. The Spark ML improvements further allow business and stakeholders to obtain insights much faster and derive greater value from their Data Science teams.

Suggested Articles

Speak Your Mind

*