Open source platform for the machine learning lifecycle
Any CI/CD experience you had working as a conventional SDE should translate well to "MLOps". Here are some resources to help you review what kinds of considerations might be important for productionizing ML projects: https://mlflow.org/, https://docs.microsoft.com/en-us/azure/machine-learning/concept-model-management-and-deployment
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Much of the material from that book is publicly available in this repo maintained by the author.
Static code analysis for 29 languages.. Your projects are multi-language. So is SonarQube analysis. Find Bugs, Vulnerabilities, Security Hotspots, and Code Smells so you can release quality code every time. Get started analyzing your projects today for free.
Common statistical tests are linear models (or: how to teach stats)
Don't know about any cheat sheet but perhaps you'd find this pretty stimulating to read: https://lindeloev.github.io/tests-as-linear/
[RFP] Product idea for BYOD data science platform
1 project | reddit.com/r/datascience | 17 Jun 2022
mlflow: Open source platform for the machine learning lifecycle
1 project | reddit.com/r/u_TsukiZombina | 16 May 2022
MLOps with MLflow on Kraken CI
2 projects | dev.to | 29 Apr 2022
Machine Learning adventures with MLFlow - Deploying models from local system to Production
1 project | reddit.com/r/learnmachinelearning | 22 Dec 2021
How to store preprocessing and feature engineering pipeline?
1 project | reddit.com/r/datascience | 21 Oct 2021