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Cyclops offers a range of tools that make it easier to handle complex ML deployments, monitor applications, and ensure everything runs smoothly.
Example Hello World war-file for testing purposes.
Jfrog Container Registry
SonarQube deployment
This project features a Next Word Prediction Model deployed via a FLASK API, implemented using a Bi-LSTM model with an attention layer.
🧟 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! 💪
Dockerizing a deep learning model
Installation of Cascade with docker-compose
Backend deployment repo on render for Todo App
Webhook deploy using flask
My Demo Repo For Gatsby Cloud Deployment. Used Gatsby's Demo Stater Blog Page for Example
End-to-end machine learning regression model with heroku deployment for predicting flight ticket prices.
As a part of ATS Infotech AI & ML course, trained a model for Flight Fare Prediction and deployed on Heroku!
Example Repo for the Udemy Course "Deployment of Machine Learning Models"
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
🚀 Simple deployment tool, that works painlessly
We've got a workflow problem. And we're fixing it - here’s Enrol (step 1).
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