{"id":30130944,"url":"https://github.com/ravin-d-27/mlops_project","last_synced_at":"2026-06-15T20:31:43.910Z","repository":{"id":213540629,"uuid":"733343556","full_name":"ravin-d-27/MLOps_Project","owner":"ravin-d-27","description":"Production Level MLOps Project for Titanic Dataset","archived":false,"fork":false,"pushed_at":"2023-12-26T11:58:14.000Z","size":59,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-08-10T18:37:07.018Z","etag":null,"topics":["docker","machine-learning","mlflow","mlflow-tracking","mlops","scikit","zenml"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ravin-d-27.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2023-12-19T05:43:17.000Z","updated_at":"2023-12-21T17:54:31.000Z","dependencies_parsed_at":"2023-12-26T12:57:47.767Z","dependency_job_id":null,"html_url":"https://github.com/ravin-d-27/MLOps_Project","commit_stats":null,"previous_names":["ravin-d-27/mlops_project"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ravin-d-27/MLOps_Project","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ravin-d-27%2FMLOps_Project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ravin-d-27%2FMLOps_Project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ravin-d-27%2FMLOps_Project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ravin-d-27%2FMLOps_Project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ravin-d-27","download_url":"https://codeload.github.com/ravin-d-27/MLOps_Project/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ravin-d-27%2FMLOps_Project/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34379915,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-15T02:00:07.085Z","response_time":63,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["docker","machine-learning","mlflow","mlflow-tracking","mlops","scikit","zenml"],"created_at":"2025-08-10T18:35:11.668Z","updated_at":"2026-06-15T20:31:43.896Z","avatar_url":"https://github.com/ravin-d-27.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MLOps Project for Titanic Dataset using ZenML and ML Flow\n\nThis repository contains the implementation of Titanic Survival Prediction using Scikit-learn. But the Catch here is I have written `Production Level Code`, which can be made live in your local server and see its stats using `ZenML` and `MLFlow`.\n\n# Requirements:\n\n- `Python 3.x`\n- `Linux or Mac Environment (In windows, use WSL. In my case, I have used Linux to develop this)`\n- `Decent specs to run python scripts, as the ML model is just SVM`\n\n# Training Pipeline\n\nThis script, `training_pipeline.py`, is a ZenML pipeline that orchestrates the process of training a machine learning model. It includes the following steps:\n\n1. **Data Ingestion**: The `run` function from `steps.ingest_data` module is used to ingest data from a given data path.\n\n2. **Data Cleaning**: The `clean_data` function from `steps.clean_data` module is used to clean the ingested data.\n\n3. **Data Splitting**: The `split_data` function from `steps.split_data` module is used to split the cleaned data into features (X) and target (y).\n\n4. **Train and Test Split**: The `train_and_test_split` function from `steps.train_and_test_split` module is used to split the data into training and testing sets.\n\n5. **Model Training**: The `train_model` function from `steps.model_train` module is used to train the model using the training data.\n\n6. **Model Evaluation**: The `model_eval` function from `steps.model_eval` module is used to evaluate the trained model using the testing data.\n\n## Usage\n\nThis pipeline is decorated with the `@pipeline` decorator from ZenML, and takes a single argument: `data_path`, which is the path to the data to be ingested.\n\n```python\n@pipeline\ndef training_pipeline(data_path: str):\n    ...\n```\n\nTo run this pipeline, you would typically import it in another script and call it with the path to your data:\n```python\nfrom training_pipeline import training_pipeline\ntraining_pipeline('path/to/your/data.csv')\n```\n\n### Important Commands\n`zenml up` - To turn up the server\u003cbr\u003e\n`zenml down` - To turn down the server of zenml\u003cbr\u003e\n`zenml disconnect` - to disconnect zenml server\u003cbr\u003e\n`zenml init` - To initialize the zenml folder\u003cbr\u003e\n`zenml stack describe`- To see the stack description\u003cbr\u003e\n`zenml stack list` - Lists down the stack names along with Stack ID and which stack is active\n\nThe project can only be executed with a ZenML stack that has an MLflow experiment tracker and model deployer as a component. Configuring a new stack with the two components are as follows:\n\n`zenml integration install mlflow -y`\u003cbr\u003e\n`zenml experiment-tracker register mlflow_tracker --flavor=mlflow`\u003cbr\u003e\n`zenml model-deployer register mlflow --flavor=mlflow`\u003cbr\u003e\n`zenml stack register mlflow_stack -a default -o default -d mlflow -e mlflow_tracker --set`\u003cbr\u003e\n`python run_deployment.py --config deploy` - To Deploy the Model\u003cbr\u003e\n`python run_deployment.py --config predict` - To Predict the results from the Model\u003cbr\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fravin-d-27%2Fmlops_project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fravin-d-27%2Fmlops_project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fravin-d-27%2Fmlops_project/lists"}