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mauseoluwasegun/README.md

👋 Hi, I'm an MLOps Engineer

I am an MLOps Engineer specializing in building end-to-end, production-grade machine learning systems.
My focus is on automation, reproducibility, scalability, and operational excellence across the ML lifecycle.

🧠 Core Competencies

  • End-to-end MLOps pipeline design
  • Model lifecycle management
  • CI/CD for ML systems
  • Infrastructure orchestration
  • Production deployment & monitoring

🛠️ Technology Stack

Machine Learning & Experimentation

  • Python
  • Scikit-learn
  • MLflow (tracking, registry)
  • DVC (data & model versioning)

APIs & Services

  • FastAPI
  • RESTful ML microservices

DevOps & Infrastructure

  • Docker
  • Kubernetes
  • CI/CD (GitHub Actions)
  • Linux environments

🔄 MLOps Workflow

  1. Data versioning with DVC
  2. Model training & experiment tracking via MLflow
  3. Model packaging as FastAPI service
  4. Containerization using Docker
  5. Deployment and scaling on Kubernetes
  6. Automated CI/CD pipelines

🚀 Featured Project

End-to-End MLOps Pipeline for Disease Prediction

A production-ready ML system demonstrating the full ML lifecycle.

Highlights:

  • Random Forest model (Scikit-learn)
  • MLflow for experiment tracking & model registry
  • DVC for dataset and artifact versioning
  • FastAPI inference service
  • Dockerized microservice
  • Kubernetes-based scalable deployment
  • CI/CD automation

This project mirrors real-world MLOps engineering practices used in production environments.

📈 Engineering Focus

  • Production reliability over experimentation-only ML
  • Infrastructure-as-code mindset
  • Monitoring, retraining, and reproducibility
  • Strong collaboration between ML and DevOps workflows

📫 Connect

⭐ If you find my work useful, consider starring or forking the repositories.

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