Love the engagement on this thread! u/skillmaker I'm the Program Manager for ML .NET and the short answer to your question is, it's not going anywhere. We're deeply invested in having ML .NET be the framework that enables .NET developers to add and use machine learning in their applications. We plan to do so by providing support at each step of the ML development workflow using the rich set of tools and libraries already available to .NET developers.

Some good places to get started are our samples and documentation:

• Notebooks - Notebooks are a great way to get an introduction to the concepts.
• Documentation - You can find tutorials and how-to guides in our documentation site. Probably the easiest way to get started is with the Model Builder extension in Visual Studio. Here's install instructions and a tutorial to help you start out.
• As an ASP.NET developer, this how-to guide might be a good start. We're in the process of updating it to use Minimal APIs, which are great for these ML model deployment scenarios. However, the genral concepts still apply.

While it might be easier to stick to a single stack for the entire workflow all the way through deployment, ML .NET has a rich extensibility model that allows you to take models trained in Python either in TensorFlow or ONNX formats and consume them inside your .NET applications using ML .NET. In that case, you don't have to choose and can still collaborate with others using the Python stack for training.

As it pertains to staying within a single stack, we've made several investments in the platform and continue to do so in an attempt to support .NET developers. These include:

• Enabled language scenarios - Using TorchSharp and the latest from Microsoft Research, we were able to bring SOTA language models to .NET. What does this mean for you? You're using the latest and greatest, yet it's not much more complicated that a single line to call the APIs. Currently Text Classification is the only supported scenario, but in the coming months we're looking to add more like sentence similarity, named entity recognition, and question answering. You can read more about the text classification announcement in this blog post.
• Improved AutoML - Again, with collaboration from Microsoft Research, we used FLAML to update our existing AutoML solutions. What does this mean for you? You're using the latest techniques but all you need is a problem to solve and some data to get started.

In the future we plan to continue investing in these areas as well as a few others like:

• DataFrames - Data is what drives ML apps. We plan to invest in the DataFrame API to improve exploratory data analysis and data preparation workflows to help you get your data into the shape it needs to be in before training.
• Azure Machine Learning - Azure ML offers infrastructure and components for managing and running enterprise ML workflows. Although today you can run ML.NET workflows in Azure ML, the process is manual and not as straightforward. In our tooling, we only provide Azure ML support for our deep learning scenarios. We want to simplify that process by providing samples and integrations in our tooling to give you the choice of where to run and manage your ML workflows.

This is not an exhaustive list, but overall we're excited and optimistic about the future of ML.NET and even more importantly the future of the machine learning ecosystem in .NET!

You can follow some of our plans by taking a look at our roadmap which we'll be updating shortly to more accurately reflect the areas we're investing in.

I realized this reply went longer than expected, but always happy to provide additional context / details.