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Category: Python

Azure ML and Azure SQL DB

I remembered that I had another blog and actually wrote something technical:

Not too long ago, I worked through an interesting issue with Azure Machine Learning. The question was, what’s the best way to read from Azure SQL Database, perform model processing, and then write results out to Azure SQL Database? Oh, by the way, I want to use a service principal rather than SQL authentication. Here’s what I’ve got.

This turned out to be a lot more work than I first expected.

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Research with R and Production with Python

Matt Dancho and Jarrell Chalmers lay out an argument:

The decision can be challenging because they both Python and R have clear strengths.

R is exceptional for Research – Making visualizations, telling the story, producing reports, and making MVP apps with Shiny. From concept (idea) to execution (code), R users tend to be able to accomplish these tasks 3X to 5X faster than Python users, making them very productive for research.

Python is exceptional for Production ML – Integrating machine learning models into production systems where your IT infrastructure relies on automation tools like Airflow or Luigi.

They make a pretty solid argument. I’ve launched success R-based projects using SQL Server Machine Learning Services, but outside of ML Services, my team’s much more likely to deploy APIs in Python, and we’re split between Dash and Shiny for visualization. H/T R-Bloggers

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ML Services: PYTHONHOME and PATH

Niels Berglund troubleshoots some issues:

In the last post, which looks at using Python 3.9 in SQL Server Machine Learning Services, I wrote this at the very end:

It looks like all is good, but maybe not? In a future post we’ll look at an issue we have introduced – but for now, let us bask in the glory of having created a new Python language extension.

In the post, we wrote a new language extension to handle Python 3.9, and that just worked fine. However, when I was doing some other things, I noticed some side effects, and in this post, we look at those side effects and how to solve them.

Click through to learn more.

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Writing a Python Language Extension for ML Services

Niels Berglund shows how you can bring your own Python 3.9 runtime to SQL Server Machine Learning Services:

When I wrote we’d look at it in a future post I thought to myself; “how hard can it be?”. I had read the steps of how to build a Python language extension for Windows here, and it didn’t seem that hard: some Boost, CMake, compile, and Bob’s your uncle! Well, it turned out it was somewhat more complicated than what I anticipated. So, if you are interested – read on!

I was going to say that the steps seem a bit complicated but not overly terrible, though Niels’s conclusion leaves me wondering.

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Using the Open Source R or Python Runtime with Machine Learning Services

Niels Berglund walks us through using the open source extensibility framework to install R or Python:

When Java became a supported language in SQL Server 2019, Microsoft mentioned that communication between ExternalHost and the language extension should be based on an API, regardless of the external language. The API is the Extensibility Framework API for SQL Server. Having an API ensures simplicity and ease of use for the extension developer.

From the paragraph above, one can assume that Microsoft would like to see 3rd party development of language extensions. That assumption turned out to be accurate as, mentioned above, Microsoft open-sourced the Java language extension, together with the include files for the extension API, in September 2020! This means that anyone interested can now create a language extension for their own favorite language!

However, open sourcing the Java extension was not the only thing Microsoft did. They also created and open-sourced language extensions for R and Python!

Click through for more detail and a walkthrough on installation of Python.

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