Does anyone actually use ML.NET?

This page summarizes the projects mentioned and recommended in the original post on /r/dotnet

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  • TorchSharp

    A .NET library that provides access to the library that powers PyTorch.

    With TorchSharp, you have access to libtorch in .NET, the library that powers PyTorch. This is what's currently backing the Text Classification API

  • spark

    .NET for Apache® Spark™ makes Apache Spark™ easily accessible to .NET developers. (by dotnet)

    Re: DataFrames, that's good to know. There is the DataFrame API which is part of the Microsoft.Data.Analysis NuGet package and that's the API that the issue is tracking and shown in the sample notebook I shared. That API has no dependencies on other systems. The DataFrame you're referring to is part of the .NET for Apache Spark library which has the dependency on Apache Spark which rqeuires some initial setup.

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  • ML.NET

    ML.NET is an open source and cross-platform machine learning framework for .NET.

    Re: ONNX, if you run into similar issues in the future, feel free to reach out in our GitHub repo or the ONNX Runtime repo and we'd be happy to help!

  • onnxruntime

    ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

    Re: ONNX, if you run into similar issues in the future, feel free to reach out in our GitHub repo or the ONNX Runtime repo and we'd be happy to help!

  • ParquetSharp.DataFrame

    ParquetSharp.DataFrame is a .NET library for reading and writing Apache Parquet files into/from .NET DataFrames, using ParquetSharp

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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