ML Model Formats and File Extensions

ML Model Formats and File Extensions

Machine Learning Model Formats and File Extensions

The realm of machine learning (ML) and artificial intelligence (AI) is marked by an array of model formats, each serving distinct purposes and ecosystems. The choice of a model format is a pivotal decision that can influence the development, deployment, and sharing of ML models. In this article, we aim to clarify the various model formats prevalent in the industry, highlighting their key characteristics, use cases, and associated file extensions. From ML.NET’s native binary format, known for its seamless integration with .NET applications, to the versatile and framework-agnostic ONNX format.

As we progress, we’ll explore each format in depth, providing you with a clear understanding of when and why to use each one. Whether you’re a seasoned data scientist, a budding ML developer, or an AI enthusiast, this guide will enhance your knowledge and proficiency in handling various ML model formats. Let’s embark on this informative journey together!

Model Formats

  • ML.NET’s Native Binary Format:
    • Used By: ML.NET framework.
    • Characteristics: This format encapsulates the machine learning model and its entire data preprocessing pipeline, tailored for .NET applications.
    • File Extension: .zip
    • Example Filename:
  • ONNX (Open Neural Network Exchange):
    • Used By: Various platforms including ML.NET, PyTorch, TensorFlow.
    • Characteristics: ONNX provides a framework-agnostic, cross-platform representation of machine learning models.
    • File Extension: .onnx
    • Example Filename: model.onnx
  • HDF5 (Hierarchical Data Format version 5):
    • Used By: Keras, TensorFlow.
    • Characteristics: Designed for storing large amounts of numerical data, including model architecture, weights, and parameters.
    • File Extension: .h5, .hdf5
    • Example Filename: model.h5
  • PMML (Predictive Model Markup Language):
    • Used By: Platforms using R and Python.
    • Characteristics: An XML-based format for representing data mining and statistical models.
    • File Extension: .xml, .pmml
    • Example Filename: model.pmml
  • Pickle:
    • Used By: Python, scikit-learn.
    • Characteristics: Python-specific format for serializing and deserializing objects.
    • File Extension: .pkl, .pickle
    • Example Filename: model.pkl
  • Protobuf (Protocol Buffers):
    • Used By: TensorFlow and other frameworks.
    • Characteristics: A binary serialization tool for structured data.
    • File Extension: .pb, .protobuf
    • Example Filename: model.pb
  • JSON (JavaScript Object Notation):
    • Used By: Various tools and platforms for storing configurations and parameters.
    • Characteristics: Widely supported and readable format.
    • File Extension: .json
    • Example Filename: config.json


In conclusion each model format has its unique strengths and use cases, ranging from ML.NET’s binary format, ideal for .NET applications, to the cross-platform ONNX format, and the widely-used HDF5 format in deep learning frameworks. The choice of format often hinges on the project’s specific needs, such as performance, interoperability, and the nature of the AI and ML tasks at hand.