mlops_worflow
============================== Local run
k port-forward svc/minio-svc -n minio 9090 9000
k port-forward svc/ml-pipeline-ui 3000:80
==============================
test from End to End Machine learning pipeline with MLOps tools
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
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├── docker <- files to create image for flask app
│ ├── templates <- html files for flask app pages
│ │ ├── home.html <- home page "/"
| | └── submit_file.html <- page to submit new data to append in dataset
│ |
│ ├── .env <- file to set env vars for flask app to access minio and github
│ ├── app.py <- main app file
│ ├── requirements.txt <- packages needed to run flask app
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├── flask <- files to deploy flask app in cluster
│ ├── cm.yml <- config map manifest
│ ├── deployment.yml
│ ├── secret.yml <- to access minio
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├── kserve <- files to test kserve
├── kubeflow <- additional files to kubeflow
├── minio <- manifests to deploy minio
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ | the creator's initials, and a short `-` delimited description, e.g.
│ | `1.0-jqp-initial-data-exploration`.
│ ├── 3s-DNN.ipynb <- notebook from youtubegoes5g
│ ├── 3sdnn.py <- just the python script parts
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├── pipeline <- files to deploy kubeflow pipeline