Leukemia Detection End to End MlOps Pipeline
Last Updated on August 29, 2025 by Editorial Team
Author(s): Ashutosh Malgaonkar
Originally published on Towards AI.
Table Of Contents
I. Data Citation
The MLOps architecture implements an end-to-end machine learning pipeline for automated leukemia classification from cell images. It effectively handles data operations, model development, model packaging, and deployment. This includes image ingestion, training a Random Forest model, optimizing for sensitivity and specificity, and packaging the model for deployment on platforms like Hugging Face. The architecture ensures that both training and validation datasets are appropriately split and evaluated for optimal performance.
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