Building a full data pipline using xray dataset "# MLOpsV2"
In this exercise, you'll gain practical experience with MLOps (Machine Learning Operations) by working on a real-world problem: classifying chest X-ray images to diagnose pneumonia. You'll go through various stages, from data cleaning to deployment, learning how to manage an end-to-end machine learning pipeline.
- Basic understanding of Python
- Familiarity with machine learning concepts
- Experience with Jupyter Notebooks
- Python
- NumPy
- OpenCV
- TensorFlow/Keras
- scikit-learn
- Flask
- Matplotlib
- imbalanced-learn
These libraries should cover most requirements for this exercise, including data manipulation (NumPy, pandas), visualization (Matplotlib), machine learning (scikit-learn, TensorFlow), image processing (OpenCV), and class imbalance treatment (imbalanced-learn).
I'll be using dvc to tack data , you can easily install it : pip install dvc
I'll be using flask api , install it with : pip install flask
here's what do looks like :
