Teams should compare MLOps platforms based on model development, experiment tracking, deployment, monitoring, automation, data management, governance, integrations, scalability, security, and cost.
- MLflow: Supports experiment tracking, model management, model registry, evaluation, and deployment workflows.
- Kubeflow: Provides Kubernetes-based pipelines, distributed training, model serving, and machine learning workflows.
- Amazon SageMaker: Offers model development, training, deployment, monitoring, pipelines, and AWS integrations.
- Google Vertex AI: Supports model training, deployment, monitoring, pipelines, and managed machine learning workflows.
- Azure Machine Learning: Provides model development, experiment tracking, deployment, monitoring, and MLOps automation.
- Databricks: Combines data engineering, machine learning, experiment tracking, model management, and governance.
- Dataiku: Supports collaborative machine learning, model development, deployment, monitoring, and governance.
- Domino Data Lab: Provides model development, collaboration, reproducibility, deployment, and enterprise governance.
- Weights & Biases: Offers experiment tracking, model evaluation, dataset management, visualization, and collaboration.
- H2O.ai: Provides AutoML, model development, deployment, monitoring, explainability, and machine learning workflows.
Teams should also compare experiment tracking, deployment automation, model monitoring, pipeline management, integrations, governance, security, scalability, collaboration, ease of use, and total cost before selecting a platform.