https://github.com/ravin-d-27/mlops_project
Production Level MLOps Project for Titanic Dataset
https://github.com/ravin-d-27/mlops_project
docker machine-learning mlflow mlflow-tracking mlops scikit zenml
Last synced: about 2 months ago
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Production Level MLOps Project for Titanic Dataset
- Host: GitHub
- URL: https://github.com/ravin-d-27/mlops_project
- Owner: ravin-d-27
- License: mit
- Created: 2023-12-19T05:43:17.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-26T11:58:14.000Z (almost 2 years ago)
- Last Synced: 2023-12-26T13:06:57.163Z (almost 2 years ago)
- Topics: docker, machine-learning, mlflow, mlflow-tracking, mlops, scikit, zenml
- Language: Python
- Homepage:
- Size: 57.6 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MLOps Project for Titanic Dataset using ZenML and ML Flow
This 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`.
# Requirements:
- `Python 3.x`
- `Linux or Mac Environment (In windows, use WSL. In my case, I have used Linux to develop this)`
- `Decent specs to run python scripts, as the ML model is just SVM`
# Training Pipeline
This script, `training_pipeline.py`, is a ZenML pipeline that orchestrates the process of training a machine learning model. It includes the following steps:
1. **Data Ingestion**: The `run` function from `steps.ingest_data` module is used to ingest data from a given data path.
2. **Data Cleaning**: The `clean_data` function from `steps.clean_data` module is used to clean the ingested data.
3. **Data Splitting**: The `split_data` function from `steps.split_data` module is used to split the cleaned data into features (X) and target (y).
4. **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.
5. **Model Training**: The `train_model` function from `steps.model_train` module is used to train the model using the training data.
6. **Model Evaluation**: The `model_eval` function from `steps.model_eval` module is used to evaluate the trained model using the testing data.
## Usage
This 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.
```python
@pipeline
def training_pipeline(data_path: str):
...
```
To run this pipeline, you would typically import it in another script and call it with the path to your data:
```python
from training_pipeline import training_pipeline
training_pipeline('path/to/your/data.csv')
```
### Important Commands
`zenml up` - To turn up the server
`zenml down` - To turn down the server of zenml
`zenml disconnect` - to disconnect zenml server
`zenml init` - To initialize the zenml folder
`zenml stack describe`- To see the stack description
`zenml stack list` - Lists down the stack names along with Stack ID and which stack is active
The 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:
`zenml integration install mlflow -y`
`zenml experiment-tracker register mlflow_tracker --flavor=mlflow`
`zenml model-deployer register mlflow --flavor=mlflow`
`zenml stack register mlflow_stack -a default -o default -d mlflow -e mlflow_tracker --set`
`python run_deployment.py --config deploy` - To Deploy the Model
`python run_deployment.py --config predict` - To Predict the results from the Model