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#2 MLflow
Open‑source platform for managing the ML lifecycle, including experiment tracking, reproducible runs, and model packaging.
#3 Neptune.ai
Experiment tracking and model registry with rich visualizations, team collaboration, and integrations for most ML libraries.
#4 Comet.ml
Unified platform for experiment tracking, model registry, dataset versioning, and real‑time monitoring of metrics.
#5 ClearML
Open‑source MLOps suite offering experiment tracking, data versioning, pipeline orchestration, and model deployment.
Git‑like version control for data, models, and experiments, enabling reproducible pipelines and remote storage.
Lightweight experiment tracking library (Sacred) paired with a web UI (Omniboard) for visualizing runs.
#8 Polyaxon
Enterprise‑grade MLOps platform for experiment tracking, hyper‑parameter tuning, and scalable pipeline orchestration on Kubernetes.
#9 TensorBoard
Built‑in visualization tool for TensorFlow (and PyTorch via torch.utils.tensorboard) offering scalars, histograms, and model graphs.
Microsoft’s end‑to‑end MLOps service with experiment tracking, automated ML, model registry, and deployment to Azure.
#11 Google Vertex AI
Google Cloud’s unified AI platform providing experiment tracking, hyper‑parameter tuning, model registry, and managed serving.
AWS service for organizing, tracking, and comparing ML experiments within SageMaker notebooks and pipelines.
Kubernetes‑native platform for building, deploying, and tracking reproducible ML pipelines with UI for run metadata.
#14 Pachyderm
Data versioning and pipeline orchestration engine that tracks data lineage alongside model experiments.
#15 Valohai
MLOps platform focused on reproducible pipelines, experiment tracking, and automated scaling on any cloud.
Self‑hosted, open‑source version of W&B offering core tracking and dashboard capabilities without a SaaS subscription.
Lightweight, open‑source experiment tracker that integrates with Git and supports offline runs.
#18 Flyte
Workflow automation platform with built‑in experiment tracking, versioned artifacts, and scalable execution on Kubernetes.
#19 Dagster
Data orchestrator that includes experiment tracking via its UI and integrates with ML libraries for pipeline monitoring.
#20 MLRun
Open‑source MLOps framework that provides experiment tracking, model serving, and automated pipelines on Kubernetes.
A free tier of Comet.ml focused solely on experiment tracking and metric logging for small teams and hobbyists.