How to Easily Deploy Machine Learning Models Using Flask - KDnuggets

When a data scientist/machine learning engineer develops a machine learning model using Scikit-Learn, TensorFlow, Keras, PyTorch etc, the ultimate goal is to make it available in production. Often times when working on a machine learning project, we focus a lot on Exploratory Data Analysis(EDA), Feature Engineering, tweaking with hyper-parameters etc. But we tend to forget our main goal, which is to extract real value from the model predictions. Deployment of machine learning models or putting models into production means making your models available to the end users or systems. However, there is complexity in the deployment of machine learning models.