#198
B
Rank #173
ZenML
An MLOps framework for building portable, reproducible machine-learning pipelines that can run on your laptop or be pushed to cloud/Kubernetes orchestrators unchanged. Its standout trait is a pluggable 'stack' abstraction decoupling pipeline code from infrastructure.
63.8
/ 100
⚙ Full Stack
Python framework with a metadata-tracking server (SQLite by default, MySQL for production) that records pipeline runs, artifacts, and stack configurations, delegating actual execution to pluggable orchestrators (local, Kubernetes, Airflow, cloud runners).
📈 Scaling Analysis
The metadata server itself is lightweight and easy to scale, and the pluggable orchestrator design means real pipeline execution can scale via whatever backend it's pointed at (Kubernetes, cloud batch); the tradeoff is that ZenML's own guarantees are only as strong as the chosen orchestrator's.
🚀 Running on Nexlayer
The ZenML server component deploys as an app pod plus `mysql.pod:3306` for production metadata storage; actual pipeline execution can be dispatched to Kubernetes Jobs in the same namespace if using the Kubernetes orchestrator plugin, keeping compute separate from the metadata server pod.