Summary

You just completed a very important part of ML Studio in this chapter. You started with an introduction to both R and Python in relation to Azure ML. You explored the importance of why you may need to extend your experiment inside ML Studio using code. Then, you learned how to execute Python scripts and import an already built code inside ML Studio. You applied the same through an example of a simple time series analysis and also created visualization with Python. After Python, you explored the same for R and performed the same tasks of time series analysis and plotted the graph with an R script. ML Studio also comes with another module to build a complete model with R apart from just running a script.

In the next chapter, you will find out how to deploy a model as a web service API from your experiment inside ML Studio, which can be consumed outside.