How to: MLOps Articles
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1. [Continuous Delivery for Machine Learning (by Thoughtworks)](https://martinfowler.com/articles/cd4ml.html)
1. [What is MLOps? NVIDIA Blog](https://blogs.nvidia.com/blog/2020/09/03/what-is-mlops/)
1. [MLSpec: A project to standardize the intercomponent schemas for a multi-stage ML Pipeline.](https://github.com/visenger/MLSpec)
1. [The 2021 State of Enterprise Machine Learning](https://info.algorithmia.com/tt-state-of-ml-2021) | State of Enterprise ML 2020: [PDF](https://info.algorithmia.com/hubfs/2019/Whitepapers/The-State-of-Enterprise-ML-2020/Algorithmia_2020_State_of_Enterprise_ML.pdf) and [Interactive](https://algorithmia.com/state-of-ml)
1. [Organizing machine learning projects: project management guidelines.](https://www.jeremyjordan.me/ml-projects-guide/)
1. [Rules for ML Project (Best practices)](http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf)
1. [ML Pipeline Template](https://www.agilestacks.com/tutorials/ml-pipelines)
1. [Data Science Project Structure](https://drivendata.github.io/cookiecutter-data-science/#directory-structure)
1. [Reproducible ML](https://github.com/cmawer/reproducible-model)
1. [ML project template facilitating both research and production phases.](https://github.com/visenger/ml-project-template)
1. [Machine learning requires a fundamentally different deployment approach. As organizations embrace machine learning, the need for new deployment tools and strategies grows.](https://www.oreilly.com/radar/machine-learning-requires-a-fundamentally-different-deployment-approach/)
1. [Introducting Flyte: A Cloud Native Machine Learning and Data Processing Platform](https://eng.lyft.com/introducing-flyte-cloud-native-machine-learning-and-data-processing-platform-fb2bb3046a59)
1. [Why is DevOps for Machine Learning so Different?](https://hackernoon.com/why-is-devops-for-machine-learning-so-different-384z32f1)
1. [Lessons learned turning machine learning models into real products and services – O’Reilly](https://www.oreilly.com/radar/lessons-learned-turning-machine-learning-models-into-real-products-and-services/)
1. [MLOps: Model management, deployment and monitoring with Azure Machine Learning](https://docs.microsoft.com/en-gb/azure/machine-learning/concept-model-management-and-deployment)
1. [Guide to File Formats for Machine Learning: Columnar, Training, Inferencing, and the Feature Store](https://towardsdatascience.com/guide-to-file-formats-for-machine-learning-columnar-training-inferencing-and-the-feature-store-2e0c3d18d4f9)
1. [Architecting a Machine Learning Pipeline How to build scalable Machine Learning systems](https://towardsdatascience.com/architecting-a-machine-learning-pipeline-a847f094d1c7)
1. [Why Machine Learning Models Degrade In Production](https://towardsdatascience.com/why-machine-learning-models-degrade-in-production-d0f2108e9214)
1. [Concept Drift and Model Decay in Machine Learning](http://xplordat.com/2019/04/25/concept-drift-and-model-decay-in-machine-learning/?source=post_page---------------------------)
1. [Machine Learning in Production: Why You Should Care About Data and Concept Drift](https://towardsdatascience.com/machine-learning-in-production-why-you-should-care-about-data-and-concept-drift-d96d0bc907fb)
1. [Bringing ML to Production](https://www.slideshare.net/mikiobraun/bringing-ml-to-production-what-is-missing-amld-2020)
1. [A Tour of End-to-End Machine Learning Platforms](https://databaseline.tech/a-tour-of-end-to-end-ml-platforms/)
1. [MLOps: Continuous delivery and automation pipelines in machine learning](https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)
1. [AI meets operations](https://www.oreilly.com/radar/ai-meets-operations/)
1. [What would machine learning look like if you mixed in DevOps? Wonder no more, we lift the lid on MLOps](https://www.theregister.co.uk/2020/03/07/devops_machine_learning_mlops/)
1. [Forbes: The Emergence Of ML Ops](https://www.forbes.com/sites/cognitiveworld/2020/03/08/the-emergence-of-ml-ops/#72f04ed04698)
1. [Cognilytica Report "ML Model Management and Operations 2020 (MLOps)"](https://www.cognilytica.com/2020/03/03/ml-model-management-and-operations-2020-mlops/) 
1. [Introducing Cloud AI Platform Pipelines](https://cloud.google.com/blog/products/ai-machine-learning/introducing-cloud-ai-platform-pipelines)
1. [A Guide to Production Level Deep Learning ](https://github.com/alirezadir/Production-Level-Deep-Learning/blob/master/README.md)
1. [The 5 Components Towards Building Production-Ready Machine Learning Systems](https://medium.com/cracking-the-data-science-interview/the-5-components-towards-building-production-ready-machine-learning-system-a4d5237ec04e)
1. [Deep Learning in Production (references about deploying deep learning-based models in production)](https://github.com/ahkarami/Deep-Learning-in-Production)
1. [Machine Learning Experiment Tracking](https://towardsdatascience.com/ma