Model Endpoints
Explore how to deploy machine learning models as web service endpoints using Flask. Learn to load and serve scikit-learn and Keras models with MLflow, handle input feature parsing, and manage TensorFlow graph contexts for scalable API predictions.
We'll cover the following...
Now that we know how to set up a web service and load pre-trained predictive models, we can set up a web service that provides a prediction result in response to a passed-in instance. We’ll deploy models for the games data set using scikit-learn and Keras.
Scikit-Learn
To use scikit-learn to host a predictive model, we’ll modify our echo service built with Flask.
The main changes we need to make are loading a scikit-learn model using MLflow, parsing out the feature vector to pass to the model from the input parameters, and adding the model result to the response payload. The updated Flask application for using scikit-learn ...