Using real-time ML Predictions Software sounds straightforward until you have to run them in production. I’ve been looking into which platforms handle this well, and here are a few that came up during my research.
AWS SageMaker – Strong for low-latency inference with scalable real-time endpoints.
Google Vertex AI – Good fit for fast online predictions and managed deployment.
Azure Machine Learning – Solid option for managed real-time endpoints, especially in Microsoft environments.
Databricks – Works well for teams combining real-time data pipelines with model serving.
H2O.ai – Flexible for API-based real-time scoring, including private deployments.
DataRobot – Useful for teams that want quick deployment and real-time prediction APIs.
Kubeflow – Best for teams that want more control over real-time inference on Kubernetes.
Curious what others have used for real-time predictions and which platforms have held up best in production?
##### Post Metadata - Posted at: 4 months ago - Net upvotes: 1 ## Comments ### Comment 1 A lot of ML platforms can score models, but real-time use cases really separate the ones built for production from the ones that are mostly training-focused. ##### Comment Metadata - Posted at: 3 months ago ## Related discussions - [How well does Trello scale into a larger team?](https://www.g2.com/discussions/1-how-well-does-trello-scale-into-a-larger-team) - Posted at: about 13 years ago - Comments: 6 - [Can we please add a new section](https://www.g2.com/discussions/2-can-we-please-add-a-new-section) - Posted at: about 13 years ago - Comments: 0 - [Quantifiable benefits from implementing your CRM](https://www.g2.com/discussions/quantifiable-benefits-from-implementing-your-crm) - Posted at: about 13 years ago - Comments: 4