Hello, MLOps!

MLOps sits at the intersection of machine learning, software engineering, and operations. I am interested in productionizing models, streamlining CI/CD for ML, and building reliable data and serving pipelines.

On this blog I will share notes and examples on topics like:

• Containerizing models with Docker and serving them with Kubernetes
• Experiment tracking and model registry practices using MLflow
• Feature and data pipelines plus orchestration with Airflow
• CI/CD for ML covering training, validation, and deployment
• Observability, metrics, and drift detection in production

Stay tuned!