Example Hello World war-file for testing purposes.

🚀 Simple deployment tool, that works painlessly

🧟 Zombie Detector Web App 📊 Predicts survival chances in a zombie apocalypse scenario. 🌍 Prepare yourself for the undead with tips and resources. 🔮 Built with Flask, scikit-learn, and JavaScript. 🖥️ Deployed on a web server. 💻 Join the fight against the walking dead! 💪

Jfrog Container Registry

We've got a workflow problem. And we're fixing it - here’s Enrol (step 1).

My Demo Repo For Gatsby Cloud Deployment. Used Gatsby's Demo Stater Blog Page for Example

The React Admin Dashboard is a sleek, responsive web application built with React, Vite, and Tailwind CSS, featuring stunning animations powered by motion. It offers a seamless and dynamic user experience with fluid transitions and modern design. ✨📊 To enjoy it live! Click on the link given below👇

SonarQube deployment

The final stage of the ML lifecycle is the deployment. For that, the solution notebook has to be converted into a format that could be scalable. The basic approach is to convert the file to 3 different files that is a configuration file, preprocessing file & pipeline file

Welcome to the magical realm of colors! This React Color Picker App is your new go-to tool for all things colorful. This app allows you to explore & customize colors effortlessly through an intuitive interface. Dive into the vibrant world of React development with this fun and interactive project. To experience in live go to the link given below👇

Dockerizing a deep learning model

A new homogeneous way to deploy your heterogeneous apps

This project features a Next Word Prediction Model deployed via a FLASK API, implemented using a Bi-LSTM model with an attention layer.

End-to-end machine learning regression model with heroku deployment for predicting flight ticket prices.

Example Repo for the Udemy Course "Deployment of Machine Learning Models"

Webhook deploy using flask

Installation of Cascade with docker-compose

Backend deployment repo on render for Todo App

Early fire detection is crucial for minimizing damage and saving lives. Traditional methods like smoke detectors and manual monitoring often suffer from delays and inaccuracies. Deep learning-based approaches offer real-time detection with higher precision, analyzing patterns from images and videos effectively.