True Multicloud Machine Learning

Spend less and produce better outcomes from machine learning projects using InfinStor's unique compute and storage capabilities.

faster Model deployment

InfinStor storage innovations help you iterate faster on larger datasets and produce more accurate models.


Once deployed, automatically monitor your model for Data Drift using our patent pending storage innovation InfinSlice.

Data Provenance

Author transformations in Jupyter notebook cells and then apply them in a scale-out architecture for perfect data provenance.

MLflow Integration

InfinStor extensions to industry standard MLflow enable you to manage the complete workflow.

The Complete Multicloud ML Platform

InfinStor’s patent-pending storage and compute innovations enable data scientists to train using bigger datasets at lower compute costs. Enjoy significant cost savings as InfinStor automatically relocates training and inference tasks to the cheapest GPU spot instances in AWS, Azure, and Google Cloud.

How it works

Click a feature below to view its explainer video.

Prepare Data

Data prep for developing, using, and monitoring the model

Multicloud preprocessing (Linear or Graph) 

Data Provenance via InfinSnap

Develop Model

Cloud hosted JupyterLab

Multicloud training

Use Model

Model Registry

Feature Store

Monitor Model

Data Drift Protection via InfinSnap