Serving machine learning models, ONNX and other standards

In recent years, the increasing application of machine learning across domains has highlighted the need for efficient model deployment and deployment. Deployment of machine learning models involves making trained models suitable for inference in real-world applications. This process is critical to integrating ML solutions into everyday products and services, such as recommendation systems, autonomous vehicles, and health diagnostics. However, challenges such as interoperability, scalability, and performance optimization remain. These challenges often arise due to the different nature of machine learning frameworks and the varying requirements of production environments. Standards such as the Open Neural Network Exchange (ONNX) have emerged to address these challenges by providing a unified approach to model representation and deployment. This essay explores the role of ONNX and other standards in deploying machine learning models, analyzes their strengths and limitations, and discusses the broader landscape of model deployment practices.

The journey of machine learning models involves several stages, each with unique requirements. Typically, models are initially developed as prototypes using frameworks such as TensorFlow, PyTorch, or Scikit-learn. These frameworks provide robust tools for research, training, and experimentation. However, moving from a research environment to production poses challenges in terms of interoperability, performance, and scalability [1]. A major challenge is framework interoperability. Organizations often use multiple frameworks, each optimized for specific tasks. For example, PyTorch may be preferred for research due to its flexibility, while TensorFlow may be chosen for deployment due to its production-ready features [2, 3]. This creates a need for a standardized format that allows models to be translated and used across frameworks and platforms without significant overhead. In addition, deploying models to production environments requires low latency, especially for applications that require real-time prediction. This requires not only optimized deployment pipelines, but also the ability to efficiently utilize hardware accelerators such as GPUs, TPUs, and custom ASICs. As ML solutions continue to support millions of users, it becomes increasingly important to ensure reliability, security, and efficient use of resources. In response to these requirements, standardized tools and formats have emerged that bridge the gap between training and deployment while ensuring seamless integration into various production systems [4].

ONNX (Open Neural Network Exchange) is an open-source format designed to enable the exchange of machine learning and deep learning models across various frameworks. Developed by Microsoft and Facebook in 2017, ONNX allows models trained in one framework to be exported and used in another, fostering greater interoperability and flexibility. [5] ONNX represents machine learning models as computational graphs, where nodes denote operations and edges denote the data flow between these operations. This provides compatibility across different frameworks and platforms, allowing models to be exported from one framework and imported into another with minimal changes. The process starts by exporting a trained model from the source framework to the ONNX format. At the same time, the model architecture, weights and associated metadata are serialized according to the ONNX specification. The model can then be executed using an ONNX-compatible runtime, such as the ONNX Runtime, or further transformed for deployment on specific hardware accelerators [6]. ONNX includes an extensive set of operators that define the basic elements of machine learning models. These operators are continuously extended to support new features and to ensure compatibility with the latest advances in deep learning. If a model contains non-standard or unsupported operators, developers can extend ONNX by defining new operator implementations [7]. ONNX provides significant benefits, making it a pivotal tool in the machine learning ecosystem. It excels in enabling seamless interoperability by allowing models to be converted between frameworks like PyTorch and TensorFlow, which fosters collaboration and reduces development effort. Additionally, ONNX Runtime allows models to leverage hardware-specific optimizations, resulting in faster inference times and improved resource efficiency. Furthermore, as an open-source standard, ONNX encourages community-driven contributions, such as new operators and enhancements, which continuously expand its capabilities and adaptability to cutting-edge machine learning advancements [8]. Despite its many advantages, ONNX is not without its limitations. One issue is the incomplete support for all operators and features available in individual frameworks. This is a potential bias in model accuracy after transformation, which may require additional validation and tuning.

While ONNX is the leading model interoperability standard, several other standards and tools aim to address similar issues in the machine learning ecosystem. A notable example is the TensorFlow SavedModel format, which serves as an end-to-end solution for exporting and deploying TensorFlow models. SavedModel includes the model architecture, weights, and associated metadata, allowing for seamless integration with TensorFlow Serving and other TensorFlow tools. However, it is largely tied to the TensorFlow ecosystem, limiting its flexibility compared to ONNX [9]. OpenVINO (Open Visual Inference and Neural Network Optimization), developed by Intel, is another noteworthy tool. It is designed to optimize and deploy models on Intel hardware, accelerating deep learning inference across various use cases, such as generative AI, video, audio, and language with models from popular frameworks like PyTorch, TensorFlow, ONNX, and more. OpenVINO is widely used in industries that require high-speed, low-latency inference, such as IoT and healthcare [10]. Apache TVM, an open-source machine learning compiler stack, focuses on optimizing deep learning models for deployment across diverse hardware platforms, including CPUs, GPUs, and specialized accelerators. It is particularly well-suited for applications requiring cross-platform compatibility and performance tuning [11]. TorchScript, a feature of PyTorch, is another alternative. It allows PyTorch models to be serialized and run independently of the PyTorch framework. TorchScript supports optimizations and hardware compatibility but lacks the cross-framework flexibility that ONNX provides [12]. Core ML by Apple focuses on optimizing machine learning models for deployment on Apple devices. It provides a highly efficient runtime for iOS and macOS applications but is limited in scope due to its exclusivity to Apple's ecosystem [13].

The future of serving machine learning models lies in enhancing interoperability, efficiency, and scalability. As the demand for real-time, on-device inference grows, the focus will shift toward optimizing models for edge computing and IoT devices. Lightweight formats and runtime optimizations will play a crucial role in ensuring that models can perform efficiently under resource-constrained environments. This trend will drive further integration of ONNX with hardware accelerators and emerging technologies like quantum computing, expanding its applicability across diverse domains [14]. Federated learning will be another critical area of development, necessitating robust frameworks for decentralized model training and serving. The rise of privacy-preserving techniques, such as secure multi-party computation and differential privacy, will require serving frameworks to adapt to these requirements while maintaining efficiency and scalability [15]. Efforts to standardize and unify existing tools and formats will also gain momentum, reducing fragmentation within the ecosystem. Collaboration between open-source communities and industry leaders will foster innovation and ensure that frameworks like ONNX remain relevant in the face of rapidly advancing machine learning technologies. Additionally, advancements in model interpretability and monitoring tools will enhance the reliability and trustworthiness of deployed models, addressing concerns related to bias and fairness.

Serving machine learning models effectively is a cornerstone of advancing AI from research into impactful real-world applications. From enabling seamless interoperability with standards like ONNX to addressing the unique requirements of edge computing, federated learning, and sustainable AI, the evolution of model serving continues to reshape the technological landscape. By optimizing performance, improving accessibility, and fostering collaborative ecosystems, the field is poised to meet the increasing demands of diverse industries and applications. ONNX, in particular, exemplifies how collaborative innovation can address pressing challenges in model deployment. Its role as a universal format not only facilitates interoperability but also drives advancements in runtime optimization and hardware acceleration. However, ONNX is just one part of a broader ecosystem of tools and standards that collectively enhance the deployment pipeline. The future of serving machine learning models will hinge on sustained collaboration between academia, industry, and open-source communities. By addressing challenges in scalability, privacy, and energy efficiency, the field can deliver intelligent systems that are both effective and sustainable. As machine learning becomes an integral part of everyday life, the frameworks and standards supporting its deployment will remain critical to ensuring reliability, efficiency, and innovation in a rapidly changing world.

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4. Yu Liu, Cheng Chen, Ru Zhang, Tingting Qin, Xiang Ji, Haoxiang Lin, Mao Yang, Enhancing the interoperability between deep learning frameworks by model conversion, URL: https://dl.acm.org/doi/abs/10.1145/3368089.3417051
5. ChatGPT 4o, What is ONNX? URL: https://chatgpt.com
6. ONNX Concepts,URL: https://onnx.ai/onnx/intro/concepts.html
7. ONNX Operators, URL: https://onnx.ai/onnx/operators/index.html
8. Understanding Open Neural Network Exchange Advantages, Dr. Jagreet Kaur Gill, URL: https://www.xenonstack.com/blog/onnx
9. Using the SavedModel format, URL: https://www.tensorflow.org/guide/saved_model
10. OpenVINO, URL: https://docs.openvino.ai/2024/index.html
11. Apache TVM, URL: https://tvm.apache.org/
12. TorchScript, URL: https://pytorch.org/docs/stable/jit.html
13. Core ML, URL: https://developer.apple.com/documentation/coreml/
14. Aymen Rayane Khouas, Mohamed Reda Bouadjenek, Hakim Hacid, and Sunil Aryal, Training Machine Learning models at the Edge: A Survey, URL: https://arxiv.org/html/2403.02619v1#S9
15. D. Zeng, S. Liang, X. Hu, H. Wang, and Z. Xu, FedLab: A Flexible Federated Learning Framework, URL: https://www.researchgate.net/publication/353478413_FedLab_A_Flexible_Federated_Learning_Framework