Features | AWS | GCP | Azure | Databricks |
Data pipeline | Data Pipeline | Dataflow | Data Factory | Spark |
Feature Store | Feature Store | --- | --- | Feature Store |
Model Monitoring | Model Monitor | --- | Azure Monitor | --- |
Experiment Management | SageMaker Experiments | --- | Azure Machine Learning SDK | MLFlow Tracking |
Model versioning | Production Variants | Versions | Model registration | MLflow Model Registry |
A/B Testing | Sagemaker | --- | Controlled Rollout | --- |
Model Serving | Sagemaker | AI Platform | Azure Machine Learning | MLFlow Model Serving |
AutoML | Autopilot | Cloud AutoML | AutomatedML | Databricks AutoML |
Notebooks | Sagemaker Notebooks | AI Platform Notebooks | Microsoft Azure Notebooks | Notebooks |
Table used in my article on TWD https://towardsdatascience.com/end-to-end-machine-learning-platforms-compared-c530d626151b
Hi Ash, thanks for putting up this comparison. This is quite good, I am writing an article on Feature Stores as well, can I back-link this image of yours?