Introduction In our last post, we demonstrated how to develop a machine learning pipeline using PyCaret and serve it as a Streamlit web application deployed onto Google Kubernetes Engine. If you haven’t heard about PyCaret before, you can read...
Introduction In my last post on deploying a machine learning pipeline in the cloud, we demonstrated how to develop a machine learning pipeline in PyCaret and deploy a trained model on Heroku PaaS as a web application built using a Streamlit open-source framework. If...
Introduction In my last post on deploying a machine learning pipeline in the cloud, I demonstrated how to develop a machine learning pipeline in PyCaret, containerize the Flask app with Docker and deploy serverless using AWS Fargate. If you haven’t heard about...
Introduction In my last post on deploying a machine learning pipeline in the cloud, we demonstrated how to develop a machine learning pipeline in PyCaret, containerize it with Docker and serve it as a web application using Google Kubernetes Engine. If you haven’t...
Introduction In my last post on deploying a machine learning pipeline in the cloud, we demonstrated how to develop a machine learning pipeline in PyCaret, containerize it with Docker and serve as a web app using Microsoft Azure Web App Services. If you haven’t...
In our last post, we demonstrated how to develop a machine learning pipeline and deploy it as a web app using PyCaret and Flask framework in Python. If you haven’t heard about PyCaret before, please read this announcement to learn more. In this tutorial, we will use...