Distributed machine learning pipelines in multi-cloud architectures: A new paradigm for data scientists

In this article, the author captures a comprehensive guide of the distributed MLP in the multi-cloud environment as critical to data scientists. The paper will discuss the existing machine learning pipelines and the realities and potentials of multi-cloud computing; the paper will also explain the propagation, uses, and effective enabling of efficient and scalable machine learning pipelines. The article describes the proper way of customised distribution of pipelines across clouds by using theoretical work, as well as the authors’ observations and examples of the use of distributed machine learning in multi-cloud environments. From the results, it is clear that this new approach can improve data handling, training, and deployment, which can advance the data science domain.