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Traditional cancer classification methods suffer from subjectivity, time-intensive processes, and a crucial need for automation. By adopting MLOps principles, we address these challenges by optimizing the end-to-end pipeline, encompassing model training, deployment, and continuous monitoring. Our goal is to provide healthcare professionals with a scalable, reproducible, and efficient tool for adenocarcinoma diagnosis, ultimately enhancing patient care and treatment outcomes.
Inspiration Can you build a machine learning model to accurately predict whether or not the patients in the dataset have cancer or not?