![]() ![]() Table 2: Pipeline input and output overviewīelow I’ll explain (1) how to create the R deployment, (2) how to add both (R and Python) deployments as objects to a pipeline and (3) how to connect both objects. Whereby we specified the following input and output data fields: Deployment The visual representation of this pipeline in the UbiOps UI looks like this:įig 1: the end result: R+Python pipeline in the UbiOps webapp. Making graphs to gain insight in the dataset and prepare the data for the prediction Please check out that notebook if you want to see how the Python deployment can be made in more detail. The Python deployment is based on the XGboost-recipe from the UbiOps cookbook. Examples of typical deployments are algorithms, data aggregation scripts and trained machine learning models. From the uploaded code, the platform will build a container, running as a microservice inside UbiOps, that can receive requests to transform input data into output data. Deployments are objects within UbiOps that serve a user’s code. It’s built up from two separate ‘deployments’. Notebook walkthrough and instructionsīefore we deep dive into how to deploy R and Python scripts in one pipeline, let’s look at the overall architecture. I could use libraries like rPython or rpy2, but it would be easier for me to use UbiOps since I am not familiar with writing code in R. A friend of mine already did the data exploration and data preparation in R but I want to make the prediction model in Python. Use caseīecause of the increase in house prices recently, I wanted to make a model that predicts house prices using this publicly available dataset. This article will show you how to create deployments and how to connect these deployments in order to create a pipeline on UbiOps. If you want to know more about the functionalities of UbiOps, you can check out the documentation page. UbiOps also saves the hassle of setting up servers, deploying our application, configuring networking, user management, scalability and uptime. ![]() This makes it much easier to integrate this pipeline into web applications or dashboards than using libraries like rpy2 or rPython, because they do not create an endpoint. Another benefit is that UbiOps enables you to immediately make requests to that pipeline via a single web service endpoint. This functionality could be very useful in situations like described above. ![]() In UbiOps it is possible to make a pipeline that consists of modular scripts called deployments, where one deployment can be written in R while the other deployment(s) can be written in Python. Ideally, you keep programming in R and your colleague can continue in Python. So you still need to have combined knowledge of R and Python. But these packages do not really offer a solution for the problem described above, because they only allow the user to import packages from the other language. There are packages like rpy2 that make it possible to use R packages within Python, or rPython which lets you use Python packages within R. When working in teams, one may encounter that different team members prefer Python or R but want to collaborate on developing and deploying a data pipeline. Python is also regarded as the best language for machine learning, thanks to packages like Scikit-learn. Python is a general-purpose language that is very readable, quick and great for mathematical computation. R is a great language to make visualizations and graphs, furthermore, it has many functionalities for data analysis. While hundreds of programming languages exist, Python and R remain the most popular ones to use in the world of data science. Combining R and Python in the same pipeline ![]()
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