# MLOps Intro

1. A [great intro about MLOPs](https://betterprogramming.pub/mlops-and-mlflops-795781d17989), what are the basic building blocks that you need to understand, in comparison to Data Engineering & DevOps - by Andrew Blance.
2. [Awesome MLOPs on github](https://github.com/visenger/awesome-mlops)
3. Analytics Vidhya
   * [A list of MLOps articles base on the "MLOps" label](https://medium.com/analytics-vidhya/tagged/mlops)
   * [A beginner guide](https://www.analyticsvidhya.com/blog/2021/06/mlops-a-beginners-guide-to-machine-learning-operations/)
   * [A comprehensive guide](https://www.analyticsvidhya.com/blog/2021/06/mlops-a-beginners-guide-to-machine-learning-operations/) - there are quite a lot of details in this article that you should know only if you truely know the basics of ML lifecycle, feature engineering, deployment strategies etc.
   * A two part series about MLOPs - Part 1: [connecting agile, data, devops, and  technology.](https://www.analyticsvidhya.com/blog/2022/02/mlops-part-1-revealing-the-approach-behind-mlops/) Part 2: [going deeper into architecture, deployment, training](https://www.analyticsvidhya.com/blog/2022/02/workflow-of-mlops-part-2-model-building/).
   * MLOps vs DevOps 
     * [The tip of the iceberg](https://www.analyticsvidhya.com/blog/2022/09/how-is-mlops-different-from-devops/) i.e., adding model + data to DevOps methodologies.
     * [Another comparison, has some more details](https://www.analyticsvidhya.com/blog/2020/11/mlops-the-why-and-the-what/) - ML challenges, components
   * [MLOps & Kubernetes ](https://www.analyticsvidhya.com/blog/2022/09/mlops-and-use-of-kubernetes/) - a very sparse intro
   * [High level E2E architecture and explanations](https://www.analyticsvidhya.com/blog/2023/02/mlops-end-to-end-mlops-architecture-and-workflow/)
   * [High level E2E concepts](https://www.analyticsvidhya.com/blog/2021/07/deepdive-into-the-emerging-concpet-of-machine-learning-operations-or-mlops/)
4. MLOps without Ops series [Part 1](https://towardsdatascience.com/mlops-without-much-ops-d17f502f76e8), [Part 2](https://towardsdatascience.com/ml-and-mlops-at-a-reasonable-scale-31d2c0782d9c), [Part 3](https://towardsdatascience.com/hagakure-for-mlops-the-four-pillars-of-ml-at-reasonable-scale-5a09bd073da), [Part 4](https://towardsdatascience.com/the-modern-data-pattern-d34d42216c81)
5. [As an engineering discipline](https://towardsdatascience.com/ml-ops-machine-learning-as-an-engineering-discipline-b86ca4874a3f)