Welcome to the MLOps Mini Projects repository! This collection of mini-projects showcases various aspects of MLOps, including deploying Machine Learning models, Natural Language Processing (NLP) projects, Deep Learning projects, and orchestrating CI/CD/CT pipelines. Each project is designed to demonstrate best MLOps practices and provide practical insights into the deployment and management of machine learning applications.
- Build and deploy an advanced IBM Watson AI chatbot using Flask : Build an advanced chatbot, utilizing IBM Watson Assistant, IBM Watson Discovery service, Rest APIs to fetch data, Flask, etc.
- Build CI/CD Pipeline for ML Models using Docker and GitHub Actions: This repository contains code and files for orchastrating CI/CD pipeline to build and deploy a Machine Learning model for predicting the survival of Titanic passengers using FastAPI, Docker and GitHub Actions.
- Dockerize ML-applications using FastAPI: This repository contains code for creating and deploying a machine learning application using FastAPI and Docker.
- Deploy Loan Approval Classifier Model on Azure Cloud: Create an end-to-end MLOps pipeline on Azure Cloud for Loan Approval Classifier Model, incorporating pre-processing, model training, and deployment.
Feel free to contribute by adding your own mini-projects to the list!
If you have a Data Science mini-project that you'd like to share, please follow the guidelines in CONTRIBUTING.md.
Please adhere to our Code of Conduct in all your interactions with the project.
This project is licensed under the MIT License.
For questions or inquiries, feel free to contact me on Linkedin.
I’m a seasoned Data Scientist and founder of TowardsMachineLearning.Org. I've worked on various Machine Learning, NLP, and cutting-edge deep learning frameworks to solve numerous business problems.