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Category: Machine Learning

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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Running an mlflow Server on Azure

Paul Hernandez configures mlflow on Azure using platform-as-a-service offerings:

It is indisputable true that mlflow came to make life a lot easier not only for data scientists but also for data engineers, architects among others. There is a very helpful list of tutorials and example in the official mlflow docs. You can just download it, open a console and start using it locally on your computer. This is the fastest way to getting started. However, as soon as you progress and introduce mlflow in your team, or you want to use it extensively for yourself, some components should be deployed outside your laptop.

To exercise a deployment setup and since I own azure experience, I decided to provision a couple of resources in the cloud to deploy the model registry and store the data produced by the tracking server.

I concur on the power of mlflow.

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Choosing an ML Algorithm

Hui Li developed a flow for determining appropriate machine learning algorithms:

Since the cheat sheet is designed for beginner data scientists and analysts, we will make some simplified assumptions when talking about the algorithms.

The algorithms recommended here result from compiled feedback and tips from several data scientists and machine learning experts and developers. There are several issues on which we have not reached an agreement and for these issues we try to highlight the commonality and reconcile the difference.

Additional algorithms will be added in later as our library grows to encompass a more complete set of available methods.

Read the whole thing.

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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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Cross-Validation in Azure ML Studio

Dinesh Asanka takes us through the cross-validation component in Azure ML Studio:

Let us look at implementing Cross-Validation in Azure Machine Learning. Let us use the sample Adventure Works database that we used for all the articles.

Then Cross Validate Model is dragged and dropped to the experiment. The Cross Validate model has two inputs and two outputs. Two inputs are data input and the relation to the Machine Learning technique. Let us use the Two-Class Decision Jungle as the Machine Learning Technique. Then the first output is connected to the Evaluate Model as shown in the following figure:

Click through for the process.

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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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TF-IDF in .NET for Spark, Updated

Ed Elliott has been busy:

Apache Spark has had a machine learning API for quite some time and this has been partially implemented in .NET for Apache Spark.

In this post we will look at how we can use the Apache Spark ML API from .NET. This is the second version of this post, the first version was written before version 1 of .NET for Apache Spark and there was a vital piece of the implementation missing which meant although we could build the model in .NET, we couldn’t actually use it. The necessary functionality is now available and so I am updating the post. To see the previous version go to: https://the.agilesql.club/2020/07/tf-idf-in-.net-for-apache-spark-using-spark-ml/

Read on for more information, as well as a call to action.

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