Building a Production-Ready MLOps Pipeline: From Data to Deployment
Over the past few weeks, I’ve been building a full-stack, production-oriented MLOps pipeline tailored for regression tasks. This is not just an academic exercise — my goal is to design and implement an end-to-end system that mirrors what real-world ML deployments demand: modularity, automation, reproducibility, and observability.
While the project is still a work in progress, it’s becoming a valuable learning ground and a personal benchmark in my MLOps journey.
🔧 What Does the Pipeline Include?
The system covers the full lifecycle of an ML project — from data ingestion to deployment, with CI/CD and monitoring in place:
- Data Processing: pandas + DVC for version-controlled pipelines, because tracking data changes is just as important as tracking code changes.
- Model Training & Registry: scikit-learn + MLflow for experiment tracking and model management, so I can compare different models and versions without getting lost.
- Online Inference API: FastAPI, containerized with Docker for scalable serving.
- Interactive Dashboard: Streamlit for simple visualization and interaction.
- Monitoring & Alerts: Prometheus + Grafana for performance metrics and system health. So I’ll know when something’s wrong before it becomes a major problem.
- CI/CD & Automation: GitHub Actions for continuous integration and deployment.
- Drift Detection: EvidentlyAI + Airflow for scheduled data and model drift checks.
💡 Why I’m Building This
There are three key motivations behind this project:
- To bridge the gap between experimentation and production — understanding how ML systems are built, deployed, and maintained in real-world scenarios.
- To get hands-on experience with modern MLOps tools and workflows — including data versioning, pipeline orchestration, observability, and CI/CD.
- To document and reflect on my learning process — including design decisions, trade-offs, implementation challenges, and lessons learned.
This project represents a milestone in my transition from model-centric development to system-level ML engineering.
📸 Architecture Snapshot
Here’s a high-level view of the current system (auto-generated and evolving):
I use visual system diagrams to better understand component relationships and to track system evolution over time. It’s an essential step in my workflow.
🛠️ It’s Not About Perfection — It’s About Progress
This project is intentionally public, imperfect, and iterative.
I’m documenting every step — the thinking behind design choices, integration pain points, and technical breakdowns — both as a reference for myself and to help others walking the same path.
Feel free to:
- Ask questions
- Share feedback
- Fork the repo and adapt it for your own use case
🔗 Project Links
GitHub: shisian512/mlops Project Focus: End-to-end regression pipeline with production-level MLOps features Upcoming Work: CI/CD extensions, real-time drift alerts, cloud deployment modules and more.
💬 Let’s Connect
Are you building MLOps pipelines, deploying ML systems, or navigating similar challenges in production ML?
I’d love to exchange ideas, hear about your experience, and learn from each other.
- 💼 Connect with me on LinkedIn — I regularly share updates, resources, and lessons from my projects.
- 📬 Prefer email? Reach me at shisian001@gmail.com — always open to thoughtful conversations and collaboration.
- ⭐️ Check out the project on GitHub — feel free to star, fork, or open an issue if you have feedback or ideas.
Whether you’re just getting started with MLOps or already deploying at scale — I’m always up for learning together.