MLOps & tracking
Connect operational practices, experiment tracking, and shared metadata to preserve the code, data, and results behind every model.
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What is MLOps?
Understand operations across the model development lifecycle.
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- Open
MLOps core principles
Connect collaboration, automation, version control, and monitoring.
- Open
ML Experiment Tracking: What to Log and How to Compare Runs
Record and compare parameters, metrics, and outputs.
- Open
ML Metadata Store: Examples, Artifacts, and Lineage
Preserve the context and lineage behind your results.
ML infrastructure explained for business teams
Assess infrastructure through delivery time, operating effort, and complete workflow costs.
Scan model artifacts before adding them to a registry
Connect model artifact scans, review evidence, and registry promotion.
Run batch LLM evaluations on Kubernetes
Preserve reproducibility across parallel evaluation workers and aggregated results.
Compare platforms
Apply the concepts above to a documented platform decision, including where each option fits and when they can coexist.
Polyaxon vs MLflow
Decide whether you need a Kubernetes AI control plane, a lifecycle metadata layer, or both.
Polyaxon vs Weights & Biases
Compare a Kubernetes workload control plane with a collaborative experiment and artifact system of record.
Polyaxon vs ClearML
Compare declarative Kubernetes workloads with Python-centric tracking and agent-driven execution.
Polyaxon vs Comet
Compare a Kubernetes workload control plane with a dedicated experiment, artifact, registry, and model-monitoring system.