What's New in ML.NET Version 1.6

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Another new release of ML.NET is now out! The release notes for version 1.6 has all the details, but this post will highlight all of the more interesting updates from this version. I'll also include the pull request for each item in case you want to see more details on it or learn how something was implemented.

There were a lot of things added to this release, but they did make a note that there are no breaking changes from everything that was added.

For the video version of this post, check below.

Support for ARM

Perhaps the most exciting part of this update is the new support for ARM architectures. This will allow for most training and inference in ML.NET.

Why is this update useful? Well, ARM architectures are almost everywhere. As mentioned in the June update blog post this ARM architectures are included on mobile and embedded devices. This can open up a whole world of opportunities for ML.NET for mobile phones and IoT devices.

DataFrame Updates

The DataFrame API is probably one of the more exciting packages that's currently in the early stages. Why? Well, .NET doesn't have much in terms of competing with pandas in Python for data analysis or data wrangling to handle some preprocessing that you may need before you send the data into ML.NET to make a model.

Why am I including DataFrame updates in a ML.NET update? Well, the DataFrame API has been moved into the ML.NET repository! The code used to be in the CoreFx Lab repository as an experimental package, but now it's no longer experimental and now part of ML.NET. This is great news since it is planned to have many more updates to this API.

Other DataFrame updates include:

  • GroupBy operation extended - While the DataFrame API already had a GroupBy operation, this update adds new property groupings and makes it act more like LINQ's GroupBy operation.
  • Improved CSV parsing - Implemented the TextFieldParser that can be used when loading a CSV file. This allows the handling of quotes in columns.
  • ConvertIDataViewtoDataFrame - We've already had a way to convert a DataFrame object into an IDataView to be able to use data loaded with the DataFrame API into ML.NET, but now we can do the opposite where we can load data in ML.NET and convert it into a DataFrame object to perform further analysis on it.
  • Improved DateTime parsing - This allows for better parsing of date time data.
  • Improvements to the Sort and Merge methods - These updates allow for better handling of null fields when performing a sort or merge.

By the way, if you're looking for a way to help contribute to the ML.NET repository, helping with the DataFrame API is a great way to get involved. They have quite a few issues already that you can take a look at and help out with. It would be awesome if we got this package on par with pandas to help make C# a great ecosystem to perform data analysis.

You can use the Microsoft.Data.Analysis label on the issues to filter them out so you can see what all they need help with.

Code Enhancements

Quite a few of the enhancement updates were code quality updates. In fact, feiyun0112 did several pull requests that improved the code quality of the repo helping to make it easier for folks to read and maintain it.

Miscellaneous Updates

There were also quite a lot of updates that didn't really tie in to a single theme. Here are some of the more interesting ones.

These are just a few of the changes in this release. Version 1.6 has a lot of stuff in it so I encourage you to go through the full release notes to see all the items that I didn't include in this post.

What was your favorite update in this release? Was it ARM support or the new DataFrame enhancements? Let me know in the comments!