# MLOps Patterns

## General Patterns

1. [ML product lifecycle patterns ](https://towardsdatascience.com/understanding-ml-product-lifecycle-patterns-a39c18302452)<br>

   <figure><img src="/files/8n7R1zvKjKp26wAOrhMn" alt=""><figcaption></figcaption></figure>
2. [ML design patterns book repo](https://github.com/GoogleCloudPlatform/ml-design-patterns)<br>

   <figure><img src="/files/zt7bPWbhZa7NkvVBKscv" alt=""><figcaption></figcaption></figure>

   <div align="left"><figure><img src="/files/Op2zx2j0j3ARRWxtU8yv" alt="" width="289"><figcaption></figcaption></figure></div>
3. [MLOPS Design Patterns](https://github.com/mercari/ml-system-design-pattern/tree/master)\
   ![](/files/7vGdRYRFgJp64ZrJrrI9)
4. [Awesome MLOps](https://github.com/visenger/awesome-mlops)

![Visenger](https://lh4.googleusercontent.com/6Dd5yQHT_iJxIGqiCSmHLs5m4nVb4by_ovEoBjrJTFcUoEvh7nmiNWpb84TJQcd5IWuSy5vElL6nFsXv5NkOKzo0Juc1ZVzX1jr3BWVgIrfhTIfGggSysNOZABG5-6h4vB8_kQ9q)

<figure><img src="/files/mfwaZdzETBrmVYxt871q" alt=""><figcaption><p>TOC</p></figcaption></figure>

4. [MLOps Roadmap](https://github.com/cdfoundation/sig-mlops/blob/main/roadmap/2022/MLOpsRoadmap2022.md)
5. [Google’s Practitioners Guide to MLOps: A framework for continuous delivery and automation of machine learning](https://cloud.google.com/resources/mlops-whitepaper)
6. [State of MLOps](https://ml-ops.org/content/state-of-mlops)<br>

   <figure><img src="/files/BnaUIWBMad6Fl1JnbnGL" alt=""><figcaption><p>Template</p></figcaption></figure>
7. [Easy mlops with pycaret and mlflow ](https://towardsdatascience.com/easy-mlops-with-pycaret-mlflow-7fbcbf1e38c6)
8. [Challenges and solutions by iguazio](https://towardsdatascience.com/ml-ops-challenges-solutions-and-future-trends-d2e59b74dc6b)

![Iguazio](https://lh3.googleusercontent.com/Pq4213qifC0KdKbweorAS7Fag6t1F1hI5eELbfWqOvQJst3tN05n4J_Sd3dyYT1Rj1NuQ7v-1Eo1x7bUCp8OGv3VSDcWy2c41lbEQjY2YmNAXyoJz9QhjgqFv5Q9QfkkacBvudZc)

## ![](https://lh3.googleusercontent.com/TqEy5NDYAnnuyM0o1j8XkKgl2KynL1Pfy6ZHG1LU7d0Ev6RtVXbCEcMFcakbPMlvYKJ41jmLDGIVazNyWA-wYEf1xKCbTzOFbJttpAp6nIWOJAvEdn1yP14NZBqXmP8b-LI80Y57)

9. [Stanford CS329](https://stanford-cs329s.github.io/syllabus.html) - CS 329S: Machine Learning Systems Design - the course goes in-depth about how ML systems are built, and how to debug, root cause, monitor, etc.
10. Metaflow, medium [1](https://medium.com/bigdatarepublic/a-review-of-netflixs-metaflow-65c6956e168d) (high level review), [2](https://medium.com/acing-ai/decoding-netflix-metaflow-2ad84b36199e) (schema), [3](https://medium.com/analytics-vidhya/metaflow-by-netflix-the-good-the-bad-and-the-ugly-b7fc6a833484), [\*\*4](https://towardsdatascience.com/learn-metaflow-in-10-mins-netflixs-python-r-framework-for-data-scientists-2ef124c716e4) (amazing), [5](https://towardsdatascience.com/be-more-efficient-to-produce-machine-learning-pipeline-with-metaflow-db5f943ebbe7) (extra), [6](https://docs.metaflow.org/metaflow/data) (loading and storing data docs!)
11. HyperparameterHunter, [Hyperopt, mlflow, unit test, concept drifts, using python and kafka](https://towardsdatascience.com/putting-ml-in-production-ii-logging-and-monitoring-algorithms-91f174044e4e)

## Patterns in Practice

1. An MLOps End-to-End system, i.e., "[You dont need a bigger boat](https://github.com/jacopotagliabue/you-dont-need-a-bigger-boat)", using MetaFlow, Snowflake, DBT, Prefect, Great Expectations, Weights & Biases, Sagemaker, Lambda
2. [A simplistic E2E system](https://github.com/jacopotagliabue/post-modern-stack) - Snowflake, DBT, S3, CometML, Reclist, SageMaker.


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