There are many moving parts in a Machine Learning model that have to be tied together for an ML model to execute and produce results successfully. This process of tying together different pieces of the ML process is known as a pipeline. A pipeline is a generalized but very important concept for a Data Scientist.
More concrete examples...
Dear
Read the publication that have the following links:
https://cloud.google.com/solutions/machine-learning/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build
https://www.itu.int/en/ITU-T/focusgroups/ml5g/Documents/ML5G-delievrables.pdf
Have a look at https://towardsdatascience.com/architecting-a-machine-learning-pipeline-a847f094d1c7
https://medium.com/analytics-vidhya/what-is-a-pipeline-in-machine-learning-how-to-create-one-bda91d0ceaca
https://cloud.google.com/ai-platform/pipelines/docs/create-pipeline
Here are some of links shedding more information about ML pipelines
https://medium.com/analytics-vidhya/what-is-a-pipeline-in-machine-learning-how-to-create-one-bda91d0ceaca
https://developers.google.com/machine-learning/testing-debugging/pipeline/overview
https://ieeexplore.ieee.org/abstract/document/5170224
Article A machine learning pipeline for quantitative phenotype predi...
Dear
Read the publication that have the following links:
https://cloud.google.com/solutions/machine-learning/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build
https://www.itu.int/en/ITU-T/focusgroups/ml5g/Documents/ML5G-delievrables.pdf
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