Integrating Machine Learning Models into Your Big Data Pipelines in Real-Time With No Coding

[Editor's note: This article was originally published on the Hortonworks Community Connection, but reproduced here because CDSW is now available on both Cloudera and Hortonworks platforms.] Using Deployed Models as a Function as a Service Using Cloudera Data Science Workbench with Apache NiFi, we can easily call functions within our deployed models from Apache NiFi as part of flows. I am working against CDSW on HDP (https://www.cloudera.com/documentation/data-science-workbench/latest/topics/cdsw_hdp.html), but it will work for all CDSW regardless of install type. In my simple example, I built a Python model that uses TextBlob to run sentiment analysis against a passed-in sentence. It returns Sentiment Polarity and Subjectivity, which we can immediately act upon in our flow. CDSW is extremely easy to work with and I was up and running in a few minutes. For my model, I created a python 3 script and a shell script for install details. Both of these artifacts are available here: https://github.

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AI and Machine Learning,CDH,Cloudera Data Science Workbench,How-to,CDSW,machine learning,models,NiFi,python,Slack,TextBlob