# Data-AI-ML Pipeline creation links ![AI artwork from boldare.com](https://i.imgur.com/r2vLD6N.png) Possibly useful for - [[Master of Philosophy - Main Page]] - [[The Golem Project Overview]] - [[GOLEM Project Page]] - [[Main AI Page]] - [[Python - Main Page]] ## List of Links Each of these pages has a backup downloaded to an [AI Tutorial Gallery in Notion](https://www.notion.so/kgbwebsinthe/340f12d89dce4d0e84ab4e8e8e859ebd?v=c17d4dfaf3014a70ab951f7278fed393). - [Architecting a Machine Learning Pipeline](https://towardsdatascience.com/architecting-a-machine-learning-pipeline-a847f094d1c7) - [Building an Automated Machine Learning Pipeline: Part One](https://towardsdatascience.com/building-an-automated-machine-learning-pipeline-part-one-5c70ae682f35) - [Building an Automated Machine Learning Pipeline: Part Two](https://towardsdatascience.com/building-an-automated-machine-learning-pipeline-part-two-1d3c86e6fe42) - [Building an Automated Machine Learning Pipeline: Part Three](https://towardsdatascience.com/building-an-automated-machine-learning-pipeline-a74acda76b98) - [Building an Automated Machine Learning Pipeline: Part Four](https://towardsdatascience.com/building-an-automated-machine-learning-pipeline-part-four-787cdc50a12d) - [Build your first Machine Learning pipeline using scikit-learn!](https://www.analyticsvidhya.com/blog/2020/01/build-your-first-machine-learning-pipeline-using-scikit-learn/) - [A Beginner’s Guide to the Data Science Pipeline](https://www.kdnuggets.com/2018/05/beginners-guide-data-science-pipeline.html) - [Testing Your Machine Learning Pipelines](https://www.kdnuggets.com/2019/11/testing-machine-learning-pipelines.html) - [A brief view of machine learning pipeline in python](https://medium.com/@yanhann10/a-brief-view-of-machine-learning-pipeline-in-python-5f50b941fca8) - [Data processing pipelines: a Swiss Army knife for data engineering](https://www.boldare.com/blog/data-processing-pipelines-in-machine-learning/) - [Putting Machine Learning Models into Production](https://blog.cloudera.com/putting-machine-learning-models-into-production/)