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Open platform for the ML lifecycle: experiment tracking, model registry, packaging, evaluation, and production monitoring.
mlopsexperiment-trackingregistrypython
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Data version control for ML: version datasets and models with Git, cloud storage, and reproducible pipelines.
mlopsdata-versioningreproducibilitypipelines
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Kubernetes-native toolkit for ML: notebooks, training jobs, pipelines, tuning, and serving components you compose on-cluster.
mlopskubernetespipelinesplatform
Also strong
Unified model serving and deployment toolkit: package models as APIs, ship to Kubernetes, and manage runtimes.
servingdeploymentmlopsapi