To see the plans for ML.NET tooling, check out the [Model Builder repo](https://github.com/dotnet/machinelearning-modelbuilder/issues?q=is%3Aissue+is%3Aopen+label%3AEpic).

As we continue to drive improvements in ML.NET and add new features, it's important to us that you're successful in adopting and using these enhanced capabilities to deliver value. Documentation and samples are an key part of that. Over the next year we plan to dedicate more resoures to deliver quality documentation and samples.

This [tracking issue](https://github.com/dotnet/docs/issues/32112) lists a few of the areas we plan to build documentation around over the next few months,

1. Continue to bring more scenario-based APIs backed by TorchSharp transformer-based architectures. The next few scenarios we're looking to enable are:

- Object detection

- Named Entity Recognition (NER)

- Question Answering

1. Enable integrations with TorchSharp for scenarios and models not supported out of the box by ML.NET.

1. Accelerate deep learning workflows by improving batch support and enabling easier use of accelerators such as ONNX Runtime Execution Providers.

LightGBM is a flexible framework for classical machine learning tasks such as classification and regression. To make the best of the features LightGBM provides, we plan to:

- Upgrade the version included in ML.NET to the latest LightGBM version

- Make interoperability with other frameworks easier by enabling saving and loading models in the native LightGBM format.

While we are working on developing the features mentioned above, we will also be working on our plan for data preparation and wrangling in .NET.

Data processing is an important part of any analytics and machine learning workflow. This process often involves loading, inspecting, transforming, and visualizing your data. We've heard your feedback that one of the ways you'd like to perform some of these tasks is by using the DataFrame API in the `Microsoft.Data.Analysis` NuGet package. This past year we worked on making the loading experience more robust and adding new column types like DateTime to enable better interoperability with the ML.NET `IDataView`. In the next year, we plan to continue focusing on the areas of:

- Improving interoperability with the `IDataView` by supporting `VBuffer` and `KeyType` columns.

- Improving stability for common operations such as loading, filtering, merging, and joining data.

In ML.NET 2.0 we made progress in this area by leveraging the `InferColumns` method as a source of information for the AutoML `Featurizer`. The `Featurizer` helps automate common data preprocessing tasks to get your data in a state that's ready for training. When used together, you don't have to define schema classes. This is convenient when working with large datasets.

However, for single predictions, there are currently no solutions. For tasks like forecasting, we've made modifications to how the PredictionEngine behaves. As a result, we expect being able to do something similar to enable untyped prediction engines.

While the details of what that implementation looks like, this year we plan to provide ways to create dynamic prediciton engines, meaning that you don't have to define your schemas beforehand and instead the shape of the training data defines the schemas.

- [#5895](https://github.com/dotnet/machinelearning/issues/5895)

In ML.NET 2.0 we made improvements to simpify the PFI API.

We also worked on porting fairness assessment and mitigation components from the [Fairlearn library to .NET](https://github.com/dotnet/machinelearning/pull/6279). Those components are not integrated into ML.NET yet. This year we plan on exposing them into ML.NET.