Current Projects & Roadmap
What I'm building and learning right now — focused on open source, ML systems, and deployment pipelines.
Open Source Contributions
Actively contributing to:
- sktime — Python library for time series machine learning, unified API for forecasting, classification, and more.
- chaoss/augur — Open source project for measuring community health and sustainability metrics.
Recent PRs: first PR on sktime, My PRs on CHAOSS/augur
Main skills: Python, unit testing, open source workflows, CI/CD.
Production Model Drift Monitoring System
Designed and built an end-to-end MLOps prototype to monitor model drift in production-like settings. The system tracks changes in prediction distributions over time and triggers alerts when significant drift is detected.
- Developed FastAPI service for model inference with logging of predictions and timestamps
- Implemented drift detection using statistical methods (PSI, KS test)
- Built interactive Streamlit dashboard with Plotly to visualize drift trends in real time
- Integrated Discord webhook alerts for threshold-based drift notifications
- Containerized application using Docker and set up basic CI using GitHub Actions
Tech Stack: FastAPI, Streamlit, Scikit-learn, Docker, GitHub Actions, SQLite, Plotly
Note: This is a prototype system focused on demonstrating end-to-end monitoring. Future improvements include better scalability, feature-level drift tracking, and performance benchmarking.
Custom Language Model Training
Trained a lightweight 4.2M-parameter language model using PyTorch to understand the end-to-end training workflow, including data preparation, model architecture, and hyperparameter tuning.
- Implemented training loop, loss tracking, and basic evaluation
- Experimented with hyperparameters such as learning rate, batch size, and sequence length
- Analyzed training behavior using loss curves and validation metrics
Currently exploring deployment aspects such as inference pipelines, latency, and system-level considerations.
Tech Stack: PyTorch, Python
Note: This project focuses on understanding training mechanics and model behavior rather than achieving state-of-the-art performance.
Learning Focus
Currently diving deep into:
- ML systems architecture and design patterns
- Production deployment strategies for ML models
- Monitoring and maintaining ML systems in production
- Cost optimization for inference at scale
Updated: February 2026