Welcome to my MLOps Projects repository! This repo contains a collection of hands-on, end-to-end projects where I apply MLOps best practices to streamline machine learning workflows using real-world tools and frameworks.
Each project demonstrates different aspects of the MLOps lifecycle — from data ingestion and versioning, to model development, CI/CD, containerization, and API deployment.
- Ingests raw files from a directory and processes them for ML readiness.
- Includes job scheduling and modular data pipelines.
- Focused on using
pandas,numpy,scikit-learn, and other key libraries. - Establishes good code hygiene and packaging standards.
- Introduces Data Version Control (DVC) to track datasets and model versions.
- Integrates with Git for end-to-end project reproducibility.
- Develops a machine learning model and deploys it as a RESTful API using FastAPI.
- Includes request validation and model inference pipeline.
- Dockerizes an entire ML workflow from training to serving.
- Emphasizes container best practices for reproducibility and portability.
- Deploy and scale ML microservices using Kubernetes, Helm, and Kustomize.
- Design reproducible training workflows with experiment tracking and auto-deployment.
- Serve trained models using managed cloud services with autoscaling and monitoring.
- Implement model health dashboards and detect prediction drift in real-time.
- Automate tuning pipelines with Optuna and track experiments using MLflow UI.
⚠️ These projects are currently in progress or planned for future development. Stay tuned by starring the repo!
- Languages: Python, Bash
- ML Libraries: scikit-learn, pandas, numpy, joblib, pydantic
- DevOps & MLOps Tools: DVC, Git, GitHub, Docker, Kubernetes, MLflow, Airflow, Prometheus
- Serving & APIs: FastAPI, Uvicorn, OpenAI
- Other Tools: Jupyter Notebooks, VS Code
MLOps is essential for scaling machine learning from notebooks to real-world production systems. These projects reflect my learning and practical experience in:
- Bridging the gap between data science and DevOps
- Automating and scaling ML workflows
- Ensuring traceability, reproducibility, and monitoring
- Building resilient, testable, and deployable ML systems
Feel free to connect with me or ask questions!
- 📧 Email: tmatin100@gmail.com
⭐ If you find this repository helpful or inspiring, please consider giving it a star. Contributions and feedback are always welcome!