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AutoGluon VS ML.NET

Compare AutoGluon VS ML.NET and see what are their differences

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Contents:
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  2. » Videos
  3. » Reviews
  4. » Alternatives

AutoGluon logo AutoGluon

Application and Data, Application Utilities, and Machine Learning Tools

ML.NET logo ML.NET

Machine Learning framework by Microsoft in .net framework and C#.
Not present
  • ML.NET Landing page
    Landing page //
    2023-03-01

AutoGluon features and specs

No features have been listed yet.

ML.NET features and specs

  • Integration with .NET Ecosystem
    ML.NET allows seamless integration with the existing .NET ecosystem, leveraging the familiarity and resources available in .NET libraries and frameworks, making it easier for developers familiar with .NET to adopt machine learning practices.
  • Support for C# and F#
    Being built primarily for .NET developers, ML.NET supports C# and F#, which means developers can build, train, and implement machine learning models using languages they are already comfortable with.
  • Open Source and Free
    ML.NET is open source, which means developers can contribute to its development, view the source code, and it's free to use without licensing costs, encouraging a community-centric approach.
  • Comprehensive Machine Learning Workflows
    ML.NET provides end-to-end support for machine learning workflows, from data preparation to model training, evaluation, and deployment, offering a range of tools and features for various types of machine learning tasks.
  • Support for AutoML
    ML.NET includes support for automated machine learning (AutoML), which simplifies model creation by automating the process of selecting algorithms and optimizing hyperparameters, making it accessible to those with less expertise in machine learning.

Possible disadvantages of ML.NET

  • Limited Community and Resources
    Compared to more established frameworks like TensorFlow or PyTorch, ML.NET has a smaller user community and fewer learning resources, which can be a constraint for beginners seeking support and documentation.
  • Less Mature Compared to Other Frameworks
    ML.NET is relatively new compared to alternatives like TensorFlow and PyTorch, which means it may be less stable and optimized for certain complex tasks or scenarios.
  • Primarily for .NET Developers
    While beneficial for .NET developers, ML.NET's strong coupling to the .NET ecosystem may not appeal to those familiar with other programming languages who may find it less intuitive or flexible.
  • Limited Support for Deep Learning
    While ML.NET provides some capabilities for deep learning, its support and performance for deep learning tasks are limited compared to dedicated deep learning frameworks like TensorFlow.
  • Dependence on .NET Runtime
    ML.NET applications require the .NET runtime, which could be seen as a dependency when deploying models outside the typical .NET environment, potentially complicating deployment scenarios across different platforms.

AutoGluon videos

+ Add

AutoML using AutoGluon

More videos:

  • Review - AutoGluon Overview ICML'20 Workshop
  • Tutorial - CVPR Tutorial: Introducing AutoGluon in 20 minutes

ML.NET videos

+ Add

Announcing ML.NET 2.0 | .NET Conf 2022

More videos:

  • Review - ML.NET Model Builder: Machine learning with .NET
  • Review - What's New in ML.NET 2.0

Category Popularity

0-100% (relative to AutoGluon and ML.NET)

AutoGluon

ML.NET

Data Science And Machine Learning
33 33%
Data Science And Machine Learning
67% 67
AI
40 40%
AI
60% 60
Machine Learning
36 36%
Machine Learning
64% 64
Data Science Tools
100 100%
Data Science Tools
0% 0

User comments

Share your experience with using AutoGluon and ML.NET. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, ML.NET should be more popular than AutoGluon. It has been mentiond 2 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

AutoGluon mentions (1)

  • Hyperparameter Optimization (HPO) using AutoGluon
    Hey Folks - I recently learned about AutoGluon (https://auto.gluon.ai) and was hoping to use it for HPO among other ML tasks! Using their quick quid, I can successfully use their TabularPredictor for my regression problem and get a number of models trained and have access to a number of details, e.g., performance, and hyperparameters used. However, using the same dataset I fail (with somewhat of a cryptic error... Source: almost 4 years ago

ML.NET mentions (2)

  • what is the future of ML.NET?
    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. Source: almost 3 years ago
  • What is the best way to get started with AI and ML in C#?
    I would start right here- ML.Net Documentation. Source: almost 4 years ago

What are some alternatives?

When comparing AutoGluon and ML.NET, you can also consider the following products

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

R MLstudio - The ML Studio is interactive for EDA, statistical modeling and machine learning applications.

R Caret - Documentation for the caret package.

datarobot - Become an AI-Driven Enterprise with Automated Machine Learning

H2O.ai - Democratizing Generative AI. Own your models: generative and predictive. We bring both super powers together with h2oGPT.

Aureo.io - Aureo.io Makes AI Simple, Fast & Easy to Integrate

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