Updating the README.md

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Author: labrijisaad

README.md

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 # `Kedro` Machine Learning Pipeline 🏯
 
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 - **CI/CD Automation**: Implement an **automated CI/CD pipeline** using `GitHub Actions` to ensure continuous testing and code quality management.
 - **Dockerization**: Develop a **Dockerized pipeline** for ease of use, incorporating `Docker volumes` for persistent data management.
 
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 ## 🛠️ Preparation & Prototyping in Notebooks
 
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 With this tool, the understanding of data progression, outputs, and interactivity is greatly simplified. Kedro Viz allows users to inspect samples of data, view parameters, analyze figures, and much more, enriching the user experience with enhanced transparency and interactivity.
 
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 ## 📜 Logging and Monitoring
 
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 Notice how the nodes are executed sequentially, and observe the **RMSE outputs during validation** for the **XGBoost model**. Logging in Kedro is highly customizable, allowing for tailored monitoring that meets the user's specific needs.
 
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 ## 📁 Project Structure
 
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 └── requirements.txt                                     # Project dependencies
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 ## 🚀 Getting Started
 
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   - For **production** environments, initialize your setup by executing `make prep-doc` or using `pip install -r docker-requirements.txt` to install the production dependencies.
   - For a **development** environment, where you may want to use **Kedro Viz**, work with **Jupyter notebooks**, or test everything thoroughly, run `make prep-dev` or `pip install -r dev-requirements.txt` to install all the development dependencies.
 
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 ### 🌿 Standard Method (Conda / venv) 
 
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 For additional assistance or to explore more command options, refer to the **Makefile** or consult `kedro --help`.
 
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 ## 🌌 Next Steps?
 With our **Kedro Pipeline** 🏗 now capable of efficiently **transforming raw** data 🔄 into **trained models** 🤖, and the introduction of a Dockerized environment 🐳 for our code, the next phase involves _advancing beyond the current repository scope_ 🚀 to `orchestrate data updates automatically` using tools like **Databricks**, **Airflow**, **Azure Data Factory**... This progression allows for the seamless integration of fresh data into our models.
 
 Moreover, implementing `experiment tracking and versioning` with **MLflow** 📊 or leveraging **Kedro Viz**'s versioning capabilities 📈 will significantly enhance our project's management and reproducibility. These steps are pivotal for maintaining a clean machine learning workflow that not only achieves our goal of simplifying model training processes 🛠 but also ensures our system remains dynamic and scalable with **minimal effort**.
 
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 ## 🌐 Let's Connect!