Enhancing Distributed Systems for Real-Time Machine Learning Model Deployment and Management

  • Authors

    • Dr. Siti Aisyah Rahman Department of Education, Nusantara State University, Kuala Lumpur, Malaysia. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2020PI1A2K

    Published 01-05-2020

  • Distributed Systems, Real-Time Processing, Machine Learning Deployment, Edge Computing, Federated Learning, System Scalability, Fault Tolerance, Dynamic Orchestration

    Issue

    Vol. 3 No. 1 (2020)

    Section

    Articles

    How to Cite

    [1]
    S. A. Rahman, “Enhancing Distributed Systems for Real-Time Machine Learning Model Deployment and Management”, IJAIDT, vol. 3, no. 1, pp. 01–07, Jan. 2020, doi: 10.67228/30713315/IJAIDT-2020PI1A2K.

  • Abstract

    The integration of machine learning (ML) models into distributed systems has become pivotal for applications requiring real-time data processing and decision-making. This paper investigates methodologies to enhance distributed architectures for the efficient deployment and management of ML models in real-time environments. We explore the challenges associated with latency, scalability, and fault tolerance, and propose solutions leveraging edge computing, federated learning, and dynamic orchestration. Through empirical evaluations, we demonstrate the efficacy of the proposed approaches in optimizing real-time ML workflows.

  • References

    [1] Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM, 51(1), 107–113.

    [2] Zaharia, M., et al. (2016). Apache Spark: A Unified Engine for Big Data Processing. Communications of the ACM, 59(11), 56–65.

    [3] Li, M., et al. (2014). Scaling Distributed Machine Learning with the Parameter Server. OSDI.

    [4] Abadi, M., et al. (2016). TensorFlow: A System for Large-Scale Machine Learning. OSDI.

    [5] Moritz, P., et al. (2018). Ray: A Distributed Framework for Emerging AI Applications. OSDI.

    [6] Crankshaw, D., et al. (2017). Clipper: A Low-Latency Online Prediction Serving System. NSDI.

    [7] Olston, C., et al. (2017). TensorFlow-Serving: Flexible, High-Performance ML Serving. arXiv preprint arXiv:1712.06139.

    [8] Kwon, Y., et al. (2019). SageMaker: Large-Scale Machine Learning in the Cloud. SIGMOD.

    [9] Oakes, E., et al. (2020). SOCC’20 – Lighthouse: Flexible and Efficient ML Serving. ACM Symposium on Cloud Computing.

  • Downloads