From d1a59560f6ebecfe8366787c7c0f2351732c2f1d Mon Sep 17 00:00:00 2001 From: Bri Achtman Date: Wed, 28 Jul 2021 16:43:13 -0500 Subject: [PATCH 01/15] Update README.md --- README.md | 51 +++++++++++++++++++++++++-------------------------- 1 file changed, 25 insertions(+), 26 deletions(-) diff --git a/README.md b/README.md index 06b5634b50..227434f6e8 100644 --- a/README.md +++ b/README.md @@ -1,33 +1,31 @@ # Machine Learning for .NET -[ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers with the same code that powers machine learning across many Microsoft products, including Power BI, Windows Defender, and Azure. +[ML.NET](https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet) is a cross-platform open-source machine learning (ML) framework for .NET. -ML.NET allows .NET developers to develop/train their own models and infuse custom machine learning into their applications using .NET, even without prior expertise in developing or tuning machine learning models. It provides data loading from files and databases, enables data transformations and includes many ML algorithms. +ML.NET allows developers to easily build, train, deploy, and consume custom models in their applications using .NET, without requireming prior expertise in developing machine learning models or experience with other programming languages like Python or R. The framework provides data loading from files and databases, enables data transformations and includes many ML algorithms. -ML.NET enables machine learning (ML) tasks like classification (for example, text classification, sentiment analysis), regression (for example, price prediction), and many other ML tasks such as anomaly detection, time-series-forecast, clustering, ranking, etc. +With ML.NET, you can train models for the following scenarios: +- Classification +- Regression +- Recommendation +- Anomaly Detection +- Time Series Forecasting +- Clustering +- Ranking +- Image classification +- Object detection -## Getting started with machine learning by using ML.NET +You can also consume both TensorFlow and ONNX models within ML.NET which makes the framework more extensible and expands the number of supported scenarios. -If you are new to machine learning, start by learning the basics from this collection of resources targeting ML.NET: +## Getting started with machine learning and ML.NET -[Learn ML.NET](https://dotnet.microsoft.com/learn/ml-dotnet) - -## ML.NET Documentation, tutorials and reference - -Please check our [documentation and tutorials](https://docs.microsoft.com/en-us/dotnet/machine-learning/). - -See the [API Reference documentation](https://docs.microsoft.com/en-us/dotnet/api/?view=ml-dotnet). - -## Sample apps - -We have a GitHub repo with [ML.NET sample apps](https://github.com/dotnet/machinelearning-samples) with many scenarios such as Sentiment analysis, Fraud detection, Product Recommender, Price Prediction, Anomaly Detection, Image Classification, Object Detection and many more. - -In addition to the ML.NET samples provided by Microsoft, we're also highlighting many more samples created by the community showcased in this separate page [ML.NET Community Samples](https://github.com/dotnet/machinelearning-samples/blob/main/docs/COMMUNITY-SAMPLES.md) - - -## ML.NET videos playlist at YouTube - -The [ML.NET videos playlist](https://aka.ms/mlnetyoutube) on YouTube contains several short videos. Each video focuses on a particular topic of ML.NET. +- Learn more about the [basics of ML.NET](https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet). +- Build your first ML.NET model by following our [ML.NET Getting Started tutorial](https://dotnet.microsoft.com/learn/ml-dotnet/get-started-tutorial/intro) +- Check out our [documentation and tutorials](https://docs.microsoft.com/en-us/dotnet/machine-learning/). +- See the [API Reference documentation](https://docs.microsoft.com/en-us/dotnet/api/?view=ml-dotnet). +- Clone our [ML.NET Samples GitHub repo](https://github.com/dotnet/machinelearning-samples) and run some sample apps. +- Take a look at some [ML.NET Community Samples](https://github.com/dotnet/machinelearning-samples/blob/main/docs/COMMUNITY-SAMPLES.md) +- Watch some videos on the [ML.NET videos YouTube playlist](https://aka.ms/mlnetyoutube). ## Operating systems and processor architectures supported by ML.NET @@ -52,7 +50,7 @@ Once you have an app, you can install the ML.NET NuGet package from the .NET Cor dotnet add package Microsoft.ML ``` -or from the NuGet package manager: +or from the NuGet Package Manager: ``` Install-Package Microsoft.ML ``` @@ -89,7 +87,8 @@ We welcome contributions! Please review our [contribution guide](CONTRIBUTING.md ## Community -Please join our community on Gitter [![Join the chat at https://gitter.im/dotnet/mlnet](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dotnet/mlnet?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) +- Join our community on [Discord]() +- Tune into the [.NET Machine Learning Community Standup](https://dotnet.microsoft.com/live/community-standup) every other Wednesday at 10AM Pacific Time. This project has adopted the code of conduct defined by the [Contributor Covenant](https://contributor-covenant.org/) to clarify expected behavior in our community. For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/code-of-conduct). @@ -97,7 +96,7 @@ For more information, see the [.NET Foundation Code of Conduct](https://dotnetfo ## Code examples -Here is a snippet code for training a model to predict sentiment from text samples. You can find complete samples in [samples repo](https://github.com/dotnet/machinelearning-samples). +Here is a snippet code for training a model to predict sentiment from text samples. You can find complete samples in the [samples repo](https://github.com/dotnet/machinelearning-samples). ```C# var dataPath = "sentiment.csv"; From a84f5950e8afe5b1a01e714fd36900a45a2705f3 Mon Sep 17 00:00:00 2001 From: Bri Achtman Date: Wed, 28 Jul 2021 16:48:33 -0500 Subject: [PATCH 02/15] Update README.md --- README.md | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 227434f6e8..2a0f6c91d7 100644 --- a/README.md +++ b/README.md @@ -2,9 +2,10 @@ [ML.NET](https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet) is a cross-platform open-source machine learning (ML) framework for .NET. -ML.NET allows developers to easily build, train, deploy, and consume custom models in their applications using .NET, without requireming prior expertise in developing machine learning models or experience with other programming languages like Python or R. The framework provides data loading from files and databases, enables data transformations and includes many ML algorithms. +ML.NET allows developers to easily build, train, deploy, and consume custom models in their applications using .NET, without requiring prior expertise in developing machine learning models or experience with other programming languages like Python or R. The framework provides data loading from files and databases, enables data transformations and includes many ML algorithms. With ML.NET, you can train models for the following scenarios: + - Classification - Regression - Recommendation @@ -20,20 +21,22 @@ You can also consume both TensorFlow and ONNX models within ML.NET which makes t ## Getting started with machine learning and ML.NET - Learn more about the [basics of ML.NET](https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet). -- Build your first ML.NET model by following our [ML.NET Getting Started tutorial](https://dotnet.microsoft.com/learn/ml-dotnet/get-started-tutorial/intro) -- Check out our [documentation and tutorials](https://docs.microsoft.com/en-us/dotnet/machine-learning/). -- See the [API Reference documentation](https://docs.microsoft.com/en-us/dotnet/api/?view=ml-dotnet). +- Build your first ML.NET model by following our [ML.NET Getting Started tutorial](https://dotnet.microsoft.com/learn/ml-dotnet/get-started-tutorial/intro). +- Check out our [documentation and tutorials](https://docs.microsoft.com/dotnet/machine-learning/). +- See the [API Reference documentation](https://docs.microsoft.com/dotnet/api/?view=ml-dotnet). - Clone our [ML.NET Samples GitHub repo](https://github.com/dotnet/machinelearning-samples) and run some sample apps. -- Take a look at some [ML.NET Community Samples](https://github.com/dotnet/machinelearning-samples/blob/main/docs/COMMUNITY-SAMPLES.md) +- Take a look at some [ML.NET Community Samples](https://github.com/dotnet/machinelearning-samples/blob/main/docs/COMMUNITY-SAMPLES.md). - Watch some videos on the [ML.NET videos YouTube playlist](https://aka.ms/mlnetyoutube). ## Operating systems and processor architectures supported by ML.NET ML.NET runs on Windows, Linux, and macOS using [.NET Core](https://github.com/dotnet/core), or Windows using .NET Framework. +ML.NET also runs on ARM64, Apple M1, and Blazor Web Assembly. However, there are some [limitations](docs/project-docs/platform-limitations). + 64 bit is supported on all platforms. 32 bit is supported on Windows, except for TensorFlow and LightGBM related functionality. -## ML.NET Nuget packages status +## ML.NET NuGet packages status [![NuGet Status](https://img.shields.io/nuget/vpre/Microsoft.ML.svg?style=flat)](https://www.nuget.org/packages/Microsoft.ML/) From d06ae852bc7dd6a5104cf2d8ba466a11165b71f4 Mon Sep 17 00:00:00 2001 From: Bri Achtman Date: Wed, 28 Jul 2021 17:18:02 -0500 Subject: [PATCH 03/15] remove cookbook link and add platform limitations doc --- README.md | 17 ++++------ docs/project-docs/platform-limitations.md | 38 +++++++++++++++++++++++ 2 files changed, 44 insertions(+), 11 deletions(-) create mode 100644 docs/project-docs/platform-limitations.md diff --git a/README.md b/README.md index 2a0f6c91d7..a765c4be1f 100644 --- a/README.md +++ b/README.md @@ -30,11 +30,11 @@ You can also consume both TensorFlow and ONNX models within ML.NET which makes t ## Operating systems and processor architectures supported by ML.NET -ML.NET runs on Windows, Linux, and macOS using [.NET Core](https://github.com/dotnet/core), or Windows using .NET Framework. +ML.NET runs on Windows, Linux, and macOS using .NET Core, or Windows using .NET Framework. -ML.NET also runs on ARM64, Apple M1, and Blazor Web Assembly. However, there are some [limitations](docs/project-docs/platform-limitations). +ML.NET also runs on ARM64, Apple M1, and Blazor Web Assembly. However, there are some [limitations](docs/project-docs/platform-limitations.md). -64 bit is supported on all platforms. 32 bit is supported on Windows, except for TensorFlow and LightGBM related functionality. +64-bit is supported on all platforms. 32-bit is supported on Windows, except for TensorFlow and LightGBM related functionality. ## ML.NET NuGet packages status @@ -90,7 +90,7 @@ We welcome contributions! Please review our [contribution guide](CONTRIBUTING.md ## Community -- Join our community on [Discord]() +- Join our community on [Discord](aka.ms/dotnet-discord). - Tune into the [.NET Machine Learning Community Standup](https://dotnet.microsoft.com/live/community-standup) every other Wednesday at 10AM Pacific Time. This project has adopted the code of conduct defined by the [Contributor Covenant](https://contributor-covenant.org/) to clarify expected behavior in our community. @@ -127,16 +127,11 @@ var prediction = predictionEngine.Predict(new SentimentData }); Console.WriteLine("prediction: " + prediction.Prediction); ``` -A cookbook that shows how to use these APIs for a variety of existing and new scenarios can be found [here](docs/code/MlNetCookBook.md). ## License -ML.NET is licensed under the [MIT license](LICENSE) and it is free to use commercially. +ML.NET is licensed under the [MIT license](LICENSE), and it is free to use commercially. ## .NET Foundation -ML.NET is a [.NET Foundation](https://www.dotnetfoundation.org/projects) project. - -There are many .NET related projects on GitHub. - -- [.NET home repo](https://github.com/Microsoft/dotnet) - links to 100s of .NET projects, from Microsoft and the community. +ML.NET is a part of the [.NET Foundation](https://www.dotnetfoundation.org/projects). \ No newline at end of file diff --git a/docs/project-docs/platform-limitations.md b/docs/project-docs/platform-limitations.md new file mode 100644 index 0000000000..1e2b0d94e1 --- /dev/null +++ b/docs/project-docs/platform-limitations.md @@ -0,0 +1,38 @@ +Contributing to Machine Learning +====================== + +This document describes contribution guidelines that are specific to Machine Learning. Please read [.NET Core Guidelines](https://github.com/dotnet/coreclr/blob/master/Documentation/project-docs/contributing.md) for more general .NET Core contribution guidelines. + +Coding Style Changes +-------------------- + +We intend to bring dotnet/machinelearning into full conformance with the style guidelines described in [Coding Style](https://github.com/dotnet/runtime/blob/main/docs/coding-guidelines/coding-style.md). We plan to do that with tooling, in a holistic way. In the meantime, please: + +* **DO NOT** send PRs for style changes. For example, do not send PRs that are focused on changing usage of ```Int32``` to ```int```. +* **DO NOT** send PRs for upgrading code to use newer language features, though it's ok to use newer language features as part of new code that's written. For example, it's ok to use expression-bodied members as part of new code you write, but do not send a PR focused on changing existing properties or methods to use the feature. +* **DO** give priority to the current style of the project or file you're changing even if it diverges from the general guidelines. + +Pull Requests +------------- + +* **DO** submit all code changes via pull requests (PRs) rather than through a direct commit. PRs will be reviewed and potentially merged by the repo maintainers after a peer review that includes at least one maintainer. +* **DO** give PRs short-but-descriptive names (for example, "Improve code coverage for System.Console by 10%", not "Fix #1234") +* **DO** refer to any relevant issues, and include [keywords](https://help.github.com/articles/closing-issues-via-commit-messages/) that automatically close issues when the PR is merged. +* **DO** tag any users that should know about and/or review the change. +* **DO** ensure each commit successfully builds. The entire PR must pass all tests in the Continuous Integration (CI) system before it'll be merged. +* **DO** address PR feedback in an additional commit(s) rather than amending the existing commits, and only rebase/squash them when necessary. This makes it easier for reviewers to track changes. +* **DO** assume that ["Squash and Merge"](https://github.com/blog/2141-squash-your-commits) will be used to merge your commit unless you request otherwise in the PR. +* **DO NOT** fix merge conflicts using a merge commit. Prefer `git rebase`. +* **DO NOT** mix independent, unrelated changes in one PR. Separate real product/test code changes from larger code formatting/dead code removal changes. Separate unrelated fixes into separate PRs, especially if they are in different assemblies. + +Merging Pull Requests (for contributors with write access) +---------------------------------------------------------- + +* **DO** use ["Squash and Merge"](https://github.com/blog/2141-squash-your-commits) by default for individual contributions unless requested by the PR author. + Do so, even if the PR contains only one commit. It creates a simpler history than "Create a Merge Commit". + Reasons that PR authors may request "Merge and Commit" may include (but are not limited to): + + - The change is easier to understand as a series of focused commits. Each commit in the series must be buildable so as not to break `git bisect`. + - Contributor is using an e-mail address other than the primary GitHub address and wants that preserved in the history. Contributor must be willing to squash + the commits manually before acceptance. + From d35c069541b10b7bc45921a89fb211e25aed9f1a Mon Sep 17 00:00:00 2001 From: Bri Achtman Date: Wed, 28 Jul 2021 17:26:43 -0500 Subject: [PATCH 04/15] Update platform-limitations.md --- docs/project-docs/platform-limitations.md | 44 ++++++----------------- 1 file changed, 11 insertions(+), 33 deletions(-) diff --git a/docs/project-docs/platform-limitations.md b/docs/project-docs/platform-limitations.md index 1e2b0d94e1..e1d9da4343 100644 --- a/docs/project-docs/platform-limitations.md +++ b/docs/project-docs/platform-limitations.md @@ -1,38 +1,16 @@ -Contributing to Machine Learning +Platform limitations ====================== -This document describes contribution guidelines that are specific to Machine Learning. Please read [.NET Core Guidelines](https://github.com/dotnet/coreclr/blob/master/Documentation/project-docs/contributing.md) for more general .NET Core contribution guidelines. +While ML.NET is cross-platform, there are some limitations for specific platforms as outlined in the chart below. -Coding Style Changes --------------------- +| | Training | Inference | +| :---- | :-----: | :---: | +| **Windows** | Yes | Yes | +| **Linux** | Yes | Yes | +| **macOS** | Yes | Yes | +| **ARM64** / **Apple M1** | Yes, with **limitations**.

The following are *not supported*:
  • Symbolic SGD
  • TensorFlow
  • OLS
  • TimeSeries SSA
  • TimeSeries SrCNN
  • ONNX*
  • Light GBM*
\**You can add support by compiling (no pre-compiled binaries provided).* | Yes, with **limitations**.

The following are *not supported*:
  • Symbolic SGD
  • TensorFlow
  • OLS
  • TimeSeries SSA
  • TimeSeries SrCNN
  • ONNX
| +| **Blazor WASM** | Yes, with **limitations**.

The following are *not supported*:
  • Symbolic SGD
  • TensorFlow
  • OLS
  • TimeSeries SSA
  • TimeSeries SrCNN
  • ONNX
  • Light GBM
  • LDA
  • Matrix Factorization
*Note: You must currently set the EnableMLUnsupportedPlatformTargetCheck flag to false to use ML.NET in Blazor.* | Yes, with **limitations**.

The following are *not supported*:
  • Symbolic SGD
  • TensorFlow
  • OLS
  • TimeSeries SSA
  • TimeSeries SrCNN
  • ONNX
  • Light GBM
  • LDA
  • Matrix Factorization
    • | -We intend to bring dotnet/machinelearning into full conformance with the style guidelines described in [Coding Style](https://github.com/dotnet/runtime/blob/main/docs/coding-guidelines/coding-style.md). We plan to do that with tooling, in a holistic way. In the meantime, please: - -* **DO NOT** send PRs for style changes. For example, do not send PRs that are focused on changing usage of ```Int32``` to ```int```. -* **DO NOT** send PRs for upgrading code to use newer language features, though it's ok to use newer language features as part of new code that's written. For example, it's ok to use expression-bodied members as part of new code you write, but do not send a PR focused on changing existing properties or methods to use the feature. -* **DO** give priority to the current style of the project or file you're changing even if it diverges from the general guidelines. - -Pull Requests -------------- - -* **DO** submit all code changes via pull requests (PRs) rather than through a direct commit. PRs will be reviewed and potentially merged by the repo maintainers after a peer review that includes at least one maintainer. -* **DO** give PRs short-but-descriptive names (for example, "Improve code coverage for System.Console by 10%", not "Fix #1234") -* **DO** refer to any relevant issues, and include [keywords](https://help.github.com/articles/closing-issues-via-commit-messages/) that automatically close issues when the PR is merged. -* **DO** tag any users that should know about and/or review the change. -* **DO** ensure each commit successfully builds. The entire PR must pass all tests in the Continuous Integration (CI) system before it'll be merged. -* **DO** address PR feedback in an additional commit(s) rather than amending the existing commits, and only rebase/squash them when necessary. This makes it easier for reviewers to track changes. -* **DO** assume that ["Squash and Merge"](https://github.com/blog/2141-squash-your-commits) will be used to merge your commit unless you request otherwise in the PR. -* **DO NOT** fix merge conflicts using a merge commit. Prefer `git rebase`. -* **DO NOT** mix independent, unrelated changes in one PR. Separate real product/test code changes from larger code formatting/dead code removal changes. Separate unrelated fixes into separate PRs, especially if they are in different assemblies. - -Merging Pull Requests (for contributors with write access) ----------------------------------------------------------- - -* **DO** use ["Squash and Merge"](https://github.com/blog/2141-squash-your-commits) by default for individual contributions unless requested by the PR author. - Do so, even if the PR contains only one commit. It creates a simpler history than "Create a Merge Commit". - Reasons that PR authors may request "Merge and Commit" may include (but are not limited to): - - - The change is easier to understand as a series of focused commits. Each commit in the series must be buildable so as not to break `git bisect`. - - Contributor is using an e-mail address other than the primary GitHub address and wants that preserved in the history. Contributor must be willing to squash - the commits manually before acceptance. +> Note: All the limitations listed above will throw a DLL not found exception. +If you are blocked by any of these limitations or would like to see different behavior when hitting them, please let us know by [filing an issue](https://github.com/dotnet/machinelearning/issues/new?assignees=&labels=&template=suggest-a-feature.md&title=). From 6aa04e6cace197e40b1da438a453f2d1e072356e Mon Sep 17 00:00:00 2001 From: Bri Achtman Date: Wed, 28 Jul 2021 17:27:26 -0500 Subject: [PATCH 05/15] Update platform-limitations.md --- docs/project-docs/platform-limitations.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/project-docs/platform-limitations.md b/docs/project-docs/platform-limitations.md index e1d9da4343..28b9cc6e7d 100644 --- a/docs/project-docs/platform-limitations.md +++ b/docs/project-docs/platform-limitations.md @@ -9,7 +9,7 @@ While ML.NET is cross-platform, there are some limitations for specific platform | **Linux** | Yes | Yes | | **macOS** | Yes | Yes | | **ARM64** / **Apple M1** | Yes, with **limitations**.

    The following are *not supported*:
    • Symbolic SGD
    • TensorFlow
    • OLS
    • TimeSeries SSA
    • TimeSeries SrCNN
    • ONNX*
    • Light GBM*
    \**You can add support by compiling (no pre-compiled binaries provided).* | Yes, with **limitations**.

    The following are *not supported*:
    • Symbolic SGD
    • TensorFlow
    • OLS
    • TimeSeries SSA
    • TimeSeries SrCNN
    • ONNX
    | -| **Blazor WASM** | Yes, with **limitations**.

    The following are *not supported*:
    • Symbolic SGD
    • TensorFlow
    • OLS
    • TimeSeries SSA
    • TimeSeries SrCNN
    • ONNX
    • Light GBM
    • LDA
    • Matrix Factorization
    *Note: You must currently set the EnableMLUnsupportedPlatformTargetCheck flag to false to use ML.NET in Blazor.* | Yes, with **limitations**.

    The following are *not supported*:
    • Symbolic SGD
    • TensorFlow
    • OLS
    • TimeSeries SSA
    • TimeSeries SrCNN
    • ONNX
    • Light GBM
    • LDA
    • Matrix Factorization
      • | +| **Blazor WASM** | Yes, with **limitations**.

      The following are *not supported*:
      • Symbolic SGD
      • TensorFlow
      • OLS
      • TimeSeries SSA
      • TimeSeries SrCNN
      • ONNX
      • Light GBM
      • LDA
      • Matrix Factorization
      *Note: You must currently set the EnableMLUnsupportedPlatformTargetCheck flag to false to use ML.NET in Blazor.* | Yes, with **limitations**.

      The following are *not supported*:
      • Symbolic SGD
      • TensorFlow
      • OLS
      • TimeSeries SSA
      • TimeSeries SrCNN
      • ONNX
      • Light GBM
      • LDA
      • Matrix Factorization
        • | > Note: All the limitations listed above will throw a DLL not found exception. From e483027002ac1a09c41d9ee3a4304d547ac73d9a Mon Sep 17 00:00:00 2001 From: Bri Achtman Date: Wed, 28 Jul 2021 17:34:38 -0500 Subject: [PATCH 06/15] remove extensions from docs index (nimbus and infer.net) --- docs/README.md | 16 +++++----------- 1 file changed, 5 insertions(+), 11 deletions(-) diff --git a/docs/README.md b/docs/README.md index da5ab98236..deb180c120 100644 --- a/docs/README.md +++ b/docs/README.md @@ -2,12 +2,12 @@ Documents Index =============== Intro to ML.NET -=============== +--------------- -ML.NET provides state-of-the-art ML algorithms, transforms and components, aiming to make them useful for all developers, data scientists, and information workers and helpful in all products, services and devices. +ML.NET provides state-of-the-art ML algorithms, transforms, and components, aiming to make them useful for all developers, data scientists, and information workers and helpful in all products, services, and devices. Project Docs -============ +------------ - [Developer Guide](project-docs/developer-guide.md) - [Contributing to ML.NET](project-docs/contributing.md) @@ -19,18 +19,12 @@ Project Docs - [ML.NET API Reference Documentation](https://docs.microsoft.com/en-us/dotnet/api/?view=ml-dotnet) Building from Source -==================== +-------------------- - [Building ML.NET on Linux and OS X](building/unix-instructions.md) - [Building ML.NET on Windows](building/windows-instructions.md) Repo of Samples -==================== +--------------- - [ML.NET Samples](https://github.com/dotnet/machinelearning-samples/blob/main/README.md) - -Extensions for ML.NET -==================== - -- [Infer.NET - Bayesian / Probabilistic inference for ML.NET](https://github.com/dotnet/infer/blob/master/README.md) -- [NimbusML - Python bindings for ML.NET](https://github.com/Microsoft/NimbusML/blob/master/README.md) From cd19cea31c85a142cb91c28774bbca03e003d41d Mon Sep 17 00:00:00 2001 From: Bri Achtman Date: Wed, 28 Jul 2021 17:48:14 -0500 Subject: [PATCH 07/15] Update README.md --- docs/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/README.md b/docs/README.md index deb180c120..3cce623465 100644 --- a/docs/README.md +++ b/docs/README.md @@ -16,7 +16,7 @@ Project Docs - [Project NuGet Dependencies](https://github.com/dotnet/buildtools/blob/master/Documentation/project-nuget-dependencies.md) - [ML.NET Roadmap](https://github.com/dotnet/machinelearning/blob/main/README.md) - [ML.NET Cookbook](code/MlNetCookBook.md) -- [ML.NET API Reference Documentation](https://docs.microsoft.com/en-us/dotnet/api/?view=ml-dotnet) +- [ML.NET API Reference Documentation](https://docs.microsoft.com/dotnet/api/?view=ml-dotnet) Building from Source -------------------- From 21870b22f3b535f380eb058f66935699ca71a67c Mon Sep 17 00:00:00 2001 From: Bri Achtman Date: Thu, 29 Jul 2021 12:29:25 -0500 Subject: [PATCH 08/15] Update README.md --- README.md | 16 +++------------- 1 file changed, 3 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index a765c4be1f..10360fd34e 100644 --- a/README.md +++ b/README.md @@ -2,19 +2,9 @@ [ML.NET](https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet) is a cross-platform open-source machine learning (ML) framework for .NET. -ML.NET allows developers to easily build, train, deploy, and consume custom models in their applications using .NET, without requiring prior expertise in developing machine learning models or experience with other programming languages like Python or R. The framework provides data loading from files and databases, enables data transformations and includes many ML algorithms. - -With ML.NET, you can train models for the following scenarios: - -- Classification -- Regression -- Recommendation -- Anomaly Detection -- Time Series Forecasting -- Clustering -- Ranking -- Image classification -- Object detection +ML.NET allows developers to easily build, train, deploy, and consume custom models in their .NET applications without requiring prior expertise in developing machine learning models or experience with other programming languages like Python or R. The framework provides data loading from files and databases, enables data transformations, and includes many ML algorithms. + +With ML.NET, you can train models for a [variety of scenarios](https://docs.microsoft.com/dotnet/machine-learning/resources/tasks), like classification, forecasting, and anomaly detection. You can also consume both TensorFlow and ONNX models within ML.NET which makes the framework more extensible and expands the number of supported scenarios. From 5e67e33dceb5ca03311f0cdbacae3b5badcb6543 Mon Sep 17 00:00:00 2001 From: Bri Achtman Date: Fri, 20 Aug 2021 15:02:11 -0500 Subject: [PATCH 09/15] Update ROADMAP.md --- ROADMAP.md | 93 +++++++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 81 insertions(+), 12 deletions(-) diff --git a/ROADMAP.md b/ROADMAP.md index 9dbc2d8016..ded850739b 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -1,21 +1,90 @@ # The ML.NET Roadmap -The goal of the ML.NET project is to make .NET developers great at machine learning. This document describes the plan for the project. +The goal of ML.NET is to democratize machine learning for .NET developers. This document outlines the current roadmap for ML.NET framework and APIs. -ML.NET is a community effort and we welcome community feedback on our plans. The best way to give feedback is to open an issue in this repo. +To see the plans for ML.NET tooling, check out the [Model Builder repo](https://github.com/dotnet/machinelearning-modelbuilder). -We also invite contributions. The [up-for-grabs issues](https://github.com/dotnet/machinelearning/issues?q=is%3Aopen+is%3Aissue+label%3Aup-for-grabs) on GitHub are a good place to start. +## Goals through June 2022 -## Goals through June 30, 2020 -### Test stability -Continuous integration builds currently have a 30% pass rate. We aim to get this pass rate up to at least 80%. +### Keep docs, samples, and repo up to date -### Streaming metrics -Currently, the way ML.NET computes [metrics](https://docs.microsoft.com/dotnet/machine-learning/resources/metrics) is memory-intensive. We will compute metrics in a streaming fashion instead, thereby reducing memory consumption. +We heard your feedback loud and clear that our outdated docs and samples were a top pain point for using ML.NET. We have invested more resources into making sure our Docs stay relevant and that we add documentation for new features faster as well as add more relevant samples. -### Multivariate anomaly detection -ML.NET already supports [univariate anomaly detection](https://docs.microsoft.com/dotnet/api/microsoft.ml.timeseriescatalog.detectanomalybysrcnn?view=ml-dotnet), but we will add the ability to detect anomalies in multiple variables over time. +You can file issues for ML.NET documentation in the [dotnet/docs repo](https://github.com/dotnet/docs) and for ML.NET samples in the [dotnet/machinelearning-samples](https://github.com/dotnet/machinelearning-samples) repo. -### ONNX Runtime exportability +We are also taking steps to organize the dotnet/machinelearning repo and updating our triage processes so that we can address your issues and feedback faster. -We will expand the number of ML.NET transforms and estimators that are exportable to the [ONNX Runtime](https://github.com/Microsoft/onnxruntime). +### Get on the .NET release schedule + +ML.NET is .NET, and just like .NET, it's here to stay. So, we've decided to align with the .NET release schedule. + +This means that we will ship our next major version of ML.NET with .NET 6.0. + +While we'll have major releases of ML.NET once a year with the major .NET releases, we will still be shipping production-ready preview version releases in between so that we can continue to deliver awesome new features throughout the year. + +### Deep learning + +This past year we've been working on our plan for deep learning in .NET, and this year we will execute that plan to expand our deep learning support. + +As part of this plan, we will: + +- Make ONNX model consumption via ML.NET easier +- Productionize [TorchSharp](https://github.com/xamarin/TorchSharp) and +- Build a bridge between TorchSharp and ML.NET. This includes using TorchSharp to power simplified ML.NET APIs for: + - Scenario-focused transfer learning scenarios (like object detection) + - Generic transfer learning for custom scenarios that don't fit the scenario-focused APIs + - Building neural networks from scratch + +Read more about the deep learning plan and leave your feedback in this [tracking issue](https://github.com/dotnet/machinelearning/issues/X). + +*Related issues*: + +- [#5372](https://github.com/dotnet/machinelearning/issues/5372) + +### New features and scenarios + +#### Named Entity Recognition (NER) + +*Related issues*: + +- [#630](https://github.com/dotnet/machinelearning/issues/630) + +#### Dynamic IDataView + +*Related issues*: + +- [#5895](https://github.com/dotnet/machinelearning/issues/5895) + +#### Multivariate time series forecasting + +*Related issues*: + +- [#5638](https://github.com/dotnet/machinelearning/issues/5638) +- [#1696](https://github.com/dotnet/machinelearning/issues/1696) + +#### Multilabel Classification + +*Related issues*: + +- [#3909](https://github.com/dotnet/machinelearning/issues/3909) + +### Model explainability & Responsible AI + +X. + +### DataFrame API + +*Related issues*: + +- [#5716](https://github.com/dotnet/machinelearning/issues/5716) +- [#1696](https://github.com/dotnet/machinelearning/issues/1696) + +### Define the plan for data prep + +While we are working on developing the features mentioned above, we will also be working on our plan for data preparation and wrangling in ML.NET. + +## Have feedback or want to contribute? + +ML.NET is a community effort and we welcome community feedback on our plans. The best way to give feedback is to [open an issue](https://github.com/dotnet/machinelearning/issues/new/choose) in this repo. + +We also invite contributions. The [up-for-grabs issues](https://github.com/dotnet/machinelearning/issues?q=is%3Aopen+is%3Aissue+label%3Aup-for-grabs) on GitHub are a good place to start. From 0d90ba278ad7516551fd002e859e1a8b8c90e88d Mon Sep 17 00:00:00 2001 From: Bri Achtman Date: Fri, 20 Aug 2021 15:43:18 -0500 Subject: [PATCH 10/15] Update ROADMAP.md --- ROADMAP.md | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/ROADMAP.md b/ROADMAP.md index ded850739b..e026917b08 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -45,6 +45,12 @@ Read more about the deep learning plan and leave your feedback in this [tracking #### Named Entity Recognition (NER) +Named Entity Recognition, or NER, is the process of identifying and classifying/tagging information in text. For example, an NER model might look at a block of text and pick out "Seattle" and "Space Needle" and categorize them as locations or might find and tag "Microsoft" as a company. + +Currently you can consume a pre-trained ONNX model in ML.NET for NER, but it is not possible to train a custom NER model in ML.NET which has been a highly requested feature for several years. + +X. + *Related issues*: - [#630](https://github.com/dotnet/machinelearning/issues/630) @@ -57,6 +63,10 @@ Read more about the deep learning plan and leave your feedback in this [tracking #### Multivariate time series forecasting +Currently ML.NET only supports univariate time series forecasting with the [SSA algorithm](https://docs.microsoft.com/dotnet/api/microsoft.ml.transforms.timeseries.ssaforecastingestimator?view=ml-dotnet) which is currently being [added to Model Builder](linkhere). + + + *Related issues*: - [#5638](https://github.com/dotnet/machinelearning/issues/5638) @@ -70,7 +80,13 @@ Read more about the deep learning plan and leave your feedback in this [tracking ### Model explainability & Responsible AI -X. +Model Explainability and Responsible AI are becoming increasingly important areas of focus in the Machine Learning space and at Microsoft. Model explainability and fairness features are important because they let you debug and improve your models and answer questions about bias, building trust, and complying with regulations. + +ML.NET currently offers two main model explainability features: [Permutation Feature Importance](https://docs.microsoft.com/dotnet/api/microsoft.ml.permutationfeatureimportanceextensions?view=ml-dotnet) (PFI) and the [Feature Contribution Calculator](https://docs.microsoft.com/dotnet/api/microsoft.ml.transforms.featurecontributioncalculatingestimator?view=ml-dotnet) (FCC). + +We got a lot of feedback that the PFI API was difficult to use, so our first step is to improve the current experience in ML.NET. These improvements can be tracked in this [issue](https://github.com/dotnet/machinelearning/issues/5625) which will be merged soon. + +This year we also plan to expand the number of model explainability and fairness features. We are currently working on this plan and will update the roadmap as we finalize which model explainability and fairness techniques we will bring into ML.NET. ### DataFrame API From 7b2202977b057717bdf4dbdf5ba94364ddc3ba88 Mon Sep 17 00:00:00 2001 From: Bri Achtman Date: Tue, 31 Aug 2021 11:19:11 -0500 Subject: [PATCH 11/15] Update ROADMAP.md --- ROADMAP.md | 73 +++++++++++++++++++++++++++++++++++------------------- 1 file changed, 47 insertions(+), 26 deletions(-) diff --git a/ROADMAP.md b/ROADMAP.md index e026917b08..ca58de3706 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -1,45 +1,42 @@ # The ML.NET Roadmap -The goal of ML.NET is to democratize machine learning for .NET developers. This document outlines the current roadmap for ML.NET framework and APIs. +The goal of ML.NET is to democratize machine learning for .NET developers. This document outlines the current roadmap for the ML.NET framework and APIs. -To see the plans for ML.NET tooling, check out the [Model Builder repo](https://github.com/dotnet/machinelearning-modelbuilder). +To see the plans for ML.NET tooling, check out the [Model Builder repo](https://github.com/dotnet/machinelearning-modelbuilder/issues/1707). ## Goals through June 2022 +The following sections outline what we plan to work on in the next year. + ### Keep docs, samples, and repo up to date -We heard your feedback loud and clear that our outdated docs and samples were a top pain point for using ML.NET. We have invested more resources into making sure our Docs stay relevant and that we add documentation for new features faster as well as add more relevant samples. +We heard your feedback loud and clear that our outdated docs and samples were a top pain point when learning and using ML.NET. + +We have invested more resources into content development to make sure our Docs stay relevant and that we add documentation for new features faster as well as add more relevant samples. You can file issues for ML.NET documentation in the [dotnet/docs repo](https://github.com/dotnet/docs) and for ML.NET samples in the [dotnet/machinelearning-samples](https://github.com/dotnet/machinelearning-samples) repo. -We are also taking steps to organize the dotnet/machinelearning repo and updating our triage processes so that we can address your issues and feedback faster. +We are also taking steps to organize the [dotnet/machinelearning](https://github.com/dotnet/machinelearning) repo and updating our triage processes so that we can address your issues and feedback faster. Issues will be linked to version releases in the [Projects](https://github.com/dotnet/machinelearning/projects) section of the repo so you can see what we're actively working on and when we plan to release. ### Get on the .NET release schedule -ML.NET is .NET, and just like .NET, it's here to stay. So, we've decided to align with the .NET release schedule. +ML.NET is .NET, and to make it feel more a part of .NET, we've decided to align with the .NET release schedule. -This means that we will ship our next major version of ML.NET with .NET 6.0. +This means that we will ship our next version of ML.NET (v1.7.0) with .NET 6.0 in November 2021. While we'll have major releases of ML.NET once a year with the major .NET releases, we will still be shipping production-ready preview version releases in between so that we can continue to deliver awesome new features throughout the year. ### Deep learning -This past year we've been working on our plan for deep learning in .NET, and this year we will execute that plan to expand our deep learning support. +This past year we've been working on our plan for deep learning in .NET, and now we are ready to execute that plan to expand ML.NET's deep learning support. As part of this plan, we will: -- Make ONNX model consumption via ML.NET easier -- Productionize [TorchSharp](https://github.com/xamarin/TorchSharp) and -- Build a bridge between TorchSharp and ML.NET. This includes using TorchSharp to power simplified ML.NET APIs for: - - Scenario-focused transfer learning scenarios (like object detection) - - Generic transfer learning for custom scenarios that don't fit the scenario-focused APIs - - Building neural networks from scratch - -Read more about the deep learning plan and leave your feedback in this [tracking issue](https://github.com/dotnet/machinelearning/issues/X). +1. Make it easier to consume ONNX models in ML.NET using the ONNX Runtime (RT) +2. Fully support and productionize [TorchSharp](https://github.com/xamarin/TorchSharp) for building neural networks in .NET +3. Build a bridge between TorchSharp and ML.NET -*Related issues*: - -- [#5372](https://github.com/dotnet/machinelearning/issues/5372) +Read more about the deep learning plan and leave your feedback in this [tracking issue](https://github.com/dotnet/machinelearning/issues/5918). ### New features and scenarios @@ -49,7 +46,7 @@ Named Entity Recognition, or NER, is the process of identifying and classifying/ Currently you can consume a pre-trained ONNX model in ML.NET for NER, but it is not possible to train a custom NER model in ML.NET which has been a highly requested feature for several years. -X. +This year, we will work on adding support for training custom NER models in ML.NET. *Related issues*: @@ -57,15 +54,21 @@ X. #### Dynamic IDataView +In ML.NET, you must first define your model input and output schemas as new classes before loading data into an IDataView. + +This year, we will work on adding a way to create dynamic IDataViews, meaning that you don't have to define your schemas beforehand and instead the shape of the training data defines the schemas. + *Related issues*: - [#5895](https://github.com/dotnet/machinelearning/issues/5895) #### Multivariate time series forecasting -Currently ML.NET only supports univariate time series forecasting with the [SSA algorithm](https://docs.microsoft.com/dotnet/api/microsoft.ml.transforms.timeseries.ssaforecastingestimator?view=ml-dotnet) which is currently being [added to Model Builder](linkhere). +Currently ML.NET only supports univariate time series forecasting with the [SSA algorithm](https://docs.microsoft.com/dotnet/api/microsoft.ml.transforms.timeseries.ssaforecastingestimator?view=ml-dotnet) which is currently being [added to Model Builder](https://github.com/dotnet/machinelearning-modelbuilder/issues/1750). +Univariate time series has one time-dependent variable whose values only depend on its past values through time. Multivariate time series has more than one time-dependent variable where each variable depends on its past values as well as the other variables. +This year, we will work on adding support for multivariate time series forecasting to ML.NET. *Related issues*: @@ -74,9 +77,24 @@ Currently ML.NET only supports univariate time series forecasting with the [SSA #### Multilabel Classification +Currently, ML.NET's classification algorithms will return one Predicted Label as well as an array of Scores which correspond to each possible class. However, mapping each label to the Score is currently not a great experience. + +This year we will work on making the prediction info more user-friendly so that it is easy to assign multiple classes to one prediction. + *Related issues*: - [#3909](https://github.com/dotnet/machinelearning/issues/3909) +- [#2278](https://github.com/dotnet/machinelearning/issues/2278) + +### Move from System.Drawing to ImageSharp + +Starting in .NET 6, System.Drawing.Common will only be supported on Windows (you can read more about this decision in this [design doc](https://github.com/dotnet/designs/blob/main/accepted/2021/system-drawing-win-only/system-drawing-win-only.md)). + +To ensure ML.NET works great on all platforms, we will replace System.Drawing with the [ImageSharp](https://github.com/SixLabors/ImageSharp) graphics library. + +*Related issues*: + +- [#3154](https://github.com/dotnet/machinelearning/issues/3154) ### Model explainability & Responsible AI @@ -88,18 +106,21 @@ We got a lot of feedback that the PFI API was difficult to use, so our first ste This year we also plan to expand the number of model explainability and fairness features. We are currently working on this plan and will update the roadmap as we finalize which model explainability and fairness techniques we will bring into ML.NET. -### DataFrame API +### Define the plan for data prep + +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. + +#### DataFrame API + +The plan for data prep will include the roadmap for the DataFrame API (Microsoft.Data.Analysis) which we will add and update to this Roadmap doc. *Related issues*: +- [#5870](https://github.com/dotnet/machinelearning/issues/5870) - [#5716](https://github.com/dotnet/machinelearning/issues/5716) - [#1696](https://github.com/dotnet/machinelearning/issues/1696) -### Define the plan for data prep - -While we are working on developing the features mentioned above, we will also be working on our plan for data preparation and wrangling in ML.NET. - -## Have feedback or want to contribute? +## Feedback and contributions ML.NET is a community effort and we welcome community feedback on our plans. The best way to give feedback is to [open an issue](https://github.com/dotnet/machinelearning/issues/new/choose) in this repo. From 884b6c050131d321d15f0770730adbbf5b0c990a Mon Sep 17 00:00:00 2001 From: Bri Achtman Date: Tue, 31 Aug 2021 11:27:35 -0500 Subject: [PATCH 12/15] Update ROADMAP.md --- ROADMAP.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ROADMAP.md b/ROADMAP.md index ca58de3706..3289ab50ca 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -14,7 +14,7 @@ We heard your feedback loud and clear that our outdated docs and samples were a We have invested more resources into content development to make sure our Docs stay relevant and that we add documentation for new features faster as well as add more relevant samples. -You can file issues for ML.NET documentation in the [dotnet/docs repo](https://github.com/dotnet/docs) and for ML.NET samples in the [dotnet/machinelearning-samples](https://github.com/dotnet/machinelearning-samples) repo. +You can file issues and make suggestions for ML.NET documentation in the [dotnet/docs repo](https://github.com/dotnet/docs) and for ML.NET samples in the [dotnet/machinelearning-samples](https://github.com/dotnet/machinelearning-samples) repo. We are also taking steps to organize the [dotnet/machinelearning](https://github.com/dotnet/machinelearning) repo and updating our triage processes so that we can address your issues and feedback faster. Issues will be linked to version releases in the [Projects](https://github.com/dotnet/machinelearning/projects) section of the repo so you can see what we're actively working on and when we plan to release. From 068170213402efe70bc377280083bdb31e8dce11 Mon Sep 17 00:00:00 2001 From: Bri Achtman Date: Fri, 3 Sep 2021 10:07:54 -0500 Subject: [PATCH 13/15] Update ROADMAP.md --- ROADMAP.md | 40 ++++++++++++++++++++++------------------ 1 file changed, 22 insertions(+), 18 deletions(-) diff --git a/ROADMAP.md b/ROADMAP.md index 3289ab50ca..d7556716c0 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -4,9 +4,19 @@ The goal of ML.NET is to democratize machine learning for .NET developers. This To see the plans for ML.NET tooling, check out the [Model Builder repo](https://github.com/dotnet/machinelearning-modelbuilder/issues/1707). +## Feedback and contributions + +ML.NET is a community effort and we welcome community feedback on our plans. The best way to give feedback is to [open an issue](https://github.com/dotnet/machinelearning/issues/new/choose) in this repo. + +We also invite contributions. The [first good issue](https://github.com/dotnet/machinelearning/labels/good%20first%20issue) and [up-for-grabs issues](https://github.com/dotnet/machinelearning/issues?q=is%3Aopen+is%3Aissue+label%3Aup-for-grabs) on GitHub are a good place to start. You can also help work on any of the features we've listed below or work on features that you want to add to the framework. + ## Goals through June 2022 -The following sections outline what we plan to work on in the next year. +The following sections outline the major areas and features we plan to work on in the next year. + +Note, that this is an aspirational list of what we hope to get to. Many of the items on this list will require more investigations and design, which can result in changes in our plans. We may have to cut things as we go, or we may be able to add more things. + +As we prioritize, cost, and continue planning, we will try to keep the Roadmap up to date to reflect our progress and learnings. ### Keep docs, samples, and repo up to date @@ -24,7 +34,7 @@ ML.NET is .NET, and to make it feel more a part of .NET, we've decided to align This means that we will ship our next version of ML.NET (v1.7.0) with .NET 6.0 in November 2021. -While we'll have major releases of ML.NET once a year with the major .NET releases, we will still be shipping production-ready preview version releases in between so that we can continue to deliver awesome new features throughout the year. +While we'll have major releases of ML.NET once a year with the major .NET releases, we will maintain release branches to optionally service ML.NET with bug fixes and/or minor features on the same cadence as .NET servicing. ### Deep learning @@ -38,6 +48,16 @@ As part of this plan, we will: Read more about the deep learning plan and leave your feedback in this [tracking issue](https://github.com/dotnet/machinelearning/issues/5918). +### Move from System.Drawing to ImageSharp + +Starting in .NET 6, System.Drawing.Common will only be supported on Windows (you can read more about this decision in this [design doc](https://github.com/dotnet/designs/blob/main/accepted/2021/system-drawing-win-only/system-drawing-win-only.md)). + +To ensure ML.NET works great on all platforms, we will replace System.Drawing with the [ImageSharp](https://github.com/SixLabors/ImageSharp) graphics library. + +*Related issues*: + +- [#3154](https://github.com/dotnet/machinelearning/issues/3154) + ### New features and scenarios #### Named Entity Recognition (NER) @@ -86,16 +106,6 @@ This year we will work on making the prediction info more user-friendly so that - [#3909](https://github.com/dotnet/machinelearning/issues/3909) - [#2278](https://github.com/dotnet/machinelearning/issues/2278) -### Move from System.Drawing to ImageSharp - -Starting in .NET 6, System.Drawing.Common will only be supported on Windows (you can read more about this decision in this [design doc](https://github.com/dotnet/designs/blob/main/accepted/2021/system-drawing-win-only/system-drawing-win-only.md)). - -To ensure ML.NET works great on all platforms, we will replace System.Drawing with the [ImageSharp](https://github.com/SixLabors/ImageSharp) graphics library. - -*Related issues*: - -- [#3154](https://github.com/dotnet/machinelearning/issues/3154) - ### Model explainability & Responsible AI Model Explainability and Responsible AI are becoming increasingly important areas of focus in the Machine Learning space and at Microsoft. Model explainability and fairness features are important because they let you debug and improve your models and answer questions about bias, building trust, and complying with regulations. @@ -119,9 +129,3 @@ The plan for data prep will include the roadmap for the DataFrame API (Microsoft - [#5870](https://github.com/dotnet/machinelearning/issues/5870) - [#5716](https://github.com/dotnet/machinelearning/issues/5716) - [#1696](https://github.com/dotnet/machinelearning/issues/1696) - -## Feedback and contributions - -ML.NET is a community effort and we welcome community feedback on our plans. The best way to give feedback is to [open an issue](https://github.com/dotnet/machinelearning/issues/new/choose) in this repo. - -We also invite contributions. The [up-for-grabs issues](https://github.com/dotnet/machinelearning/issues?q=is%3Aopen+is%3Aissue+label%3Aup-for-grabs) on GitHub are a good place to start. From e57072f198a8adc98a52e6c9ed57481795be7261 Mon Sep 17 00:00:00 2001 From: Bri Achtman Date: Wed, 8 Sep 2021 16:06:45 -0500 Subject: [PATCH 14/15] Update README.md --- README.md | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 10360fd34e..f48ead12ab 100644 --- a/README.md +++ b/README.md @@ -18,6 +18,10 @@ You can also consume both TensorFlow and ONNX models within ML.NET which makes t - Take a look at some [ML.NET Community Samples](https://github.com/dotnet/machinelearning-samples/blob/main/docs/COMMUNITY-SAMPLES.md). - Watch some videos on the [ML.NET videos YouTube playlist](https://aka.ms/mlnetyoutube). +## Roadmap + +Take a look at ML.NET's [Roadmap](ROADMAP.md) to what the team plans to work on in the next year. + ## Operating systems and processor architectures supported by ML.NET ML.NET runs on Windows, Linux, and macOS using .NET Core, or Windows using .NET Framework. @@ -32,7 +36,7 @@ ML.NET also runs on ARM64, Apple M1, and Blazor Web Assembly. However, there are ## Release notes -Check out the [release notes](docs/release-notes) to see what's new. +Check out the [release notes](docs/release-notes) to see what's new. You can also read the [blog posts](https://devblogs.microsoft.com/dotnet/category/ml-net/) for more details about each release. ## Using ML.NET packages @@ -56,7 +60,7 @@ Daily NuGet builds of the project are also available in our Azure DevOps feed: ## Building ML.NET (For contributors building ML.NET open source code) -To build ML.NET from source please visit our [developers guide](docs/project-docs/developer-guide.md). +To build ML.NET from source please visit our [developer guide](docs/project-docs/developer-guide.md). [![codecov](https://codecov.io/gh/dotnet/machinelearning/branch/main/graph/badge.svg?flag=production)](https://codecov.io/gh/dotnet/machinelearning) @@ -72,7 +76,9 @@ To build ML.NET from source please visit our [developers guide](docs/project-doc ## Release process and versioning -Check out the [release process documentation](docs/release-notes) to understand the different kinds of ML.NET releases. +Major releases of ML.NET are shipped once a year with the major .NET releases, starting with ML.NET 1.7 in November 2021 with .NET 6, then ML.NET 2.0 with .NET 7, etc. We will maintain release branches to optionally service ML.NET with bug fixes and/or minor features on the same cadence as .NET servicing. + +Check out the [Release Notes](docs/release-notes) to see all of the past ML.NET releases. ## Contributing @@ -80,16 +86,15 @@ We welcome contributions! Please review our [contribution guide](CONTRIBUTING.md ## Community -- Join our community on [Discord](aka.ms/dotnet-discord). +- Join our community on [Discord](https://aka.ms/dotnet-discord). - Tune into the [.NET Machine Learning Community Standup](https://dotnet.microsoft.com/live/community-standup) every other Wednesday at 10AM Pacific Time. This project has adopted the code of conduct defined by the [Contributor Covenant](https://contributor-covenant.org/) to clarify expected behavior in our community. For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/code-of-conduct). - ## Code examples -Here is a snippet code for training a model to predict sentiment from text samples. You can find complete samples in the [samples repo](https://github.com/dotnet/machinelearning-samples). +Here is a code snippet for training a model to predict sentiment from text samples. You can find complete samples in the [samples repo](https://github.com/dotnet/machinelearning-samples). ```C# var dataPath = "sentiment.csv"; From cc34aa55c9d421154d763d6aad2985b7ed972f51 Mon Sep 17 00:00:00 2001 From: Bri Achtman Date: Thu, 16 Sep 2021 16:49:05 -0500 Subject: [PATCH 15/15] typo in readme --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f48ead12ab..2f729cf979 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ You can also consume both TensorFlow and ONNX models within ML.NET which makes t ## Roadmap -Take a look at ML.NET's [Roadmap](ROADMAP.md) to what the team plans to work on in the next year. +Take a look at ML.NET's [Roadmap](ROADMAP.md) to see what the team plans to work on in the next year. ## Operating systems and processor architectures supported by ML.NET @@ -129,4 +129,4 @@ ML.NET is licensed under the [MIT license](LICENSE), and it is free to use comme ## .NET Foundation -ML.NET is a part of the [.NET Foundation](https://www.dotnetfoundation.org/projects). \ No newline at end of file +ML.NET is a part of the [.NET Foundation](https://www.dotnetfoundation.org/projects).