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    Review of 'Machine Learning in Production: From Experimented ML Model to System'

    Machine Learning in Production: From Experimented ML Model to SystemCrossref
    The paper is practical oriented, the workflow of ml development is clearly stated.
    Average rating:
        Rated 4.5 of 5.
    Level of importance:
        Rated 5 of 5.
    Level of validity:
        Rated 5 of 5.
    Level of completeness:
        Rated 4 of 5.
    Level of comprehensibility:
        Rated 4 of 5.
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    Reviewed article

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    Is Open Access

    Machine Learning in Production: From Experimented ML Model to System

    Production ML pipeline refers to a complete end-to-end workflow of a machine learning product ready for deployment. In recent years, companies have vastly invested in Machine Learning research; developers are developing new tools and technologies to make ML more flexible. Now, we can experience AI in most devices around us, from home appliances to cars. When we want to develop an AI-powered product, it is vital to understand the crucial workflows of the ML. Academic research to develop an ML model and a production ML pipeline are entirely different scenarios. From business problems, data collection to deploying the model is an acutely iterative process. Most of the time, Data scientists and Machine Learning Engineers need to deal with issues like data shift, concept shift, model decay, etc. Sometimes, there are need to change the complete ML architecture or how the features are engineered in the dataset. It will become tedious if someone is working in such an environment and lacks an understanding of the entire workflow of the ML pipeline. Though every ML project is different, a data scientist/ ML engineer/ data engineer must understand the end-to-end workflow of the ML pipeline for the product they are developing. The challenge starts with a business problem. We may face different domain problem statements that need to be solved with Machine Learning. How the data will be collected is also a big concern. Data pre-processing, data validation, data monitoring, feature engineering, Model Selection, hyperparameter tuning, model optimization, model performance analysis, performance evaluation, detecting bias, model deployment, post-deployment analysis & monitoring are the crucial processes to make your model production-ready. The main contribution of this research paper is to present a complete picture of the end-to-end workflows of a production-ready ML pipeline.

      Review information

      This work has been published open access under Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com.

      Artificial intelligence
      Machine Learning Pipeline, Neural Architecture Search, Principal Component Analysis, Model optimization, Dimensionality Reduction, Directed Acyclic Graph, Data Orchestrators, Model Decay
      ScienceOpen disciplines:

      Review text

      ML in Production is an important skillsets, that is must needed for ML based products development. This paper stated the process clearly, but expected little more pragmatic examples.


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