Heart Failure Prediction ML App deployed on Google Kubernetes Engine (GKE) in GCP

This video explains a complete Machine Learning end-to-end pipeline project where our goal is not only making good predictions but productionizing the entire Machine Learning pipeline, creating Web API with front end UI, creating Docker image of the entire Application, deploying the containerized ML Application onto Google Kubernetes Engine (GKE) in GCP, exposing it to Internet and finally seeing the ML web App in action from browsers and making predictions successfully. This project is based on Heart Failure Prediction dataset from Kaggle. Here, we are predicting the death event of a patient due to heart failure based on 12 clinical important features. My LinkedIn profile: / sandip-dhar-40145546
#machinelearningwithpython, #machinelearning, #ai, #gcp, #webapp, #kubernetes, #python, #docker, #flask, #swagger