README.md

mlops-rnd

Evaluation codes to design quick win MLOps architecture with open sources. All codes are tested on ml-workspace and Visual Studio Code. GCP is used for the evaluation.

Evaluation Candidates

MLOps Design Pattern

• Design Pattern : https://github.com/mercari/ml-system-design-pattern

EDA / Feature Engineering

• ml-workspace : https://github.com/ml-tooling/ml-workspace
• plotly : https://github.com/plotly/plotly_express
• lux : https://github.com/lux-org/lux
• facets : https://github.com/PAIR-code/facets
• Ray Core : https://docs.ray.io/en/latest/walkthrough.html
• Modin : https://docs.ray.io/en/latest/modin/index.html

Train

• Feast : https://docs.feast.dev/
• Tecton : https://www.tecton.ai/blog/what-is-a-feature-store/ (only available on AWS)
• Frameworks (Tensorflow, PyTorch, MXNet, Scikit Learn etc.)
• Ray SGD (TBD)

Evaluation/Test/Hyperparameter Tuning

• Tensorboard
• What-If-Tool : https://pair-code.github.io/what-if-tool/

Model Management

• keepsake : https://github.com/replicate/keepsake
• ML Metadata

Data/Notebook Versioning

• DVC : https://github.com/iterative/dvc,  https://www.analyticsvidhya.com/blog/2021/06/mlops-versioning-datasets-with-git-dvc/

Pipeline build-up/Templating

• Kubeflow
• Dockerize tools (TBD)
• cookie-cutter : https://github.com/drivendata/cookiecutter-data-science

Model Serving

• Seldon
• fastapi
• Ray serve : https://docs.ray.io/en/master/serve/index.html#

CI/CD/CT

• CML : https://github.com/iterative/cml

Monitoring & Feedback

• Evidently : https://github.com/evidentlyai/evidently

GPU Clustering

• Remote Kernel : https://github.com/tdaff/remote_ikernel
• slurm : https://slurm.schedmd.com/documentation.html

Backlog

Dataset for Research

Dataset candidates

• https://github.com/awesomedata/awesome-public-datasets
• https://towardsdatascience.com/5-data-science-projects-that-you-can-complete-over-the-weekend-34445b14707d

Merlin training with criteo dataset

• https://docs.google.com/document/d/1P_BerGSP5CNzGjGbRqgMrPcNaCmQuKUyodFaG0jlu9I/edit#heading=h.zdus0kibjxax
• https://github.com/NVIDIA-Merlin/gcp-ml-ops

Retail data

• https://www.bls.gov/cew/downloadable-data-files.htm [good]
• https://data.iowa.gov/Sales-Distribution/Iowa-Liquor-Sales/m3tr-qhgy/data [good]

MLOps based dataset

• https://github.com/GokuMohandas/MLOps

### etc

• https://github.com/dslp/dslp-repo-template
• https://github.com/academic/awesome-datascience
• https://github.com/alteryx/featuretools
• https://github.com/academic/awesome-datascience#data-sets
• https://github.com/visenger/awesome-mlops
• github actions : https://mlops.githubapp.com/examples
• https://neptune.ai/blog/best-mlops-platforms-to-manage-machine-learning-lifecycle
• https://towardsdatascience.com/mlops-practices-for-data-scientists-dbb01be45dd8
• steppy, modelstore, sacred : https://github.com/EthicalML/awesome-production-machine-learning [good]