Python is often the go-to language for machine learning, especially for training deep learning models using the PyTorch or TensorFlow libraries. Python definitely provides nice tools for deploying such models on the web as REST APIs or GUI web applications. However, models can also be exported to the ONNX format and subsequently be used for inference using an ONNX runtime. Conversion to ONNX format, as opposed to doing inference using PyTorch, is beneficial as the ONNX runtime comes in a much smaller package in terms of size and is very efficient.
Recently, there was a need for a way to cut and manipulate some timeseries data that had been collected to quantify greenhouse gas emissions. After collection, it is necessary to manually explore the data and select parts of the time-series for further analysis. This was an obvious case for an R Shiny app that could easily be shared with others and used for interactive processing of the data. Furthermore, the short time from idea –> sketch –> prototype –> test –> deployment is just incredible.
In a previous post I showed how to use R for creating bathymetric maps for lakes. To make this process even easier, I have created two apps using Shiny. The maps can be downloaded, opened in Google Earth on both desktop and mobile making it easy to bring along. Try theme out!
Shiny for interactive data exploration The R Shiny framework is a simple way to turn R analysis or pipelines into interactive web applications.