Tag Archives: python

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

Categories: AI and Machine Learning CDH Cloudera Data Science Workbench How-to

[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

104409 dataengineering 104410 datascience 104431 flowmanagement

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), 

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How to Distribute your R code with sparklyr and Cloudera Data Science Workbench

Categories: CDH How-to Spark

sparklyr is a great opportunity for R users to leverage the distributed computation power of Apache Spark without a lot of additional learning. sparklyr acts as the backend of dplyr so that R users can write almost the same code for both local and distributed calculation over Spark SQL.

 

Since sparklyr v0.6, we can run R code across our Spark cluster with spark_apply().

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Create conda recipe to use C extended Python library on PySpark cluster with Cloudera Data Science Workbench

Categories: CDH Data Science How-to Spark

Cloudera Data Science Workbench provides data scientists with secure access to enterprise data with Python, R, and Scala. In the previous article, we introduced how to use your favorite Python libraries on an Apache Spark cluster with PySpark. In Python world, data scientists often want to use Python libraries, such as XGBoost, which includes C/C++ extension. This post shows how to solve this problem creating a conda recipe with C extension.

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Use your favorite Python library on PySpark cluster with Cloudera Data Science Workbench

Categories: CDH Data Science How-to Spark

Cloudera Data Science Workbench provides freedom for data scientists. It gives them the flexibility to work with their favorite libraries using isolated environments with a container for each project.

In JVM world such as Java or Scala, using your favorite packages on a Spark cluster is easy. Each application manages preferred packages using fat JARs, and it brings independent environments with the Spark cluster. Many data scientists prefer Python to Scala for data science,

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Building a Data Science Portfolio: Storytelling with Data (Part 2: Data Exploration)

Categories: Data Science Guest

The following post (Part 2 of two parts) by Vik Paruchuri, founder of data science learning platform Dataquest, offers some detailed and instructive insight about data science workflow (regardless of the tech stack involved, but in this case, using Python). We re-publish it here for your convenience.

Before we dive into exploring the data [see Part 1 for steps relating to data preparation], we’ll want to set the context,

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