Enterprise grade security and scalability.
Available in your cloud account or as SaaS.
Experiments can be shared with chosen users and groups. Models can be shared with chosen users and groups.
No fixed Cloud Costs, pay per use. Hundreds of thousands of experiments and models.
Serverless. Not limited by traditional databases such as MySQL or PostgreSQL.
Including on wire encryption and at rest encryption.
Experience a completely reimagined MLflow.
Model and experiment sharing between team members in an organization.
All access methods are authenticated, like MLflow Web GUI, REST API, or CLI. Federate to any corporate directory.
Experiment and model authorization allow permission to create, view and manage runs model versions.
Resilience to Data Center Failure. Service is guaranteed to be up even if an Availability Zone fails. Automatic – no administrator action required. No downtime, no loss of data.
Available in three Availability Zones by default. Auto Scaling and Load Balancing, hence resilient to DDoS
Global Availability through CloudFront. Low Latency Loading of compressed assets from closest PoP
Resilience to Region Failure. Administrator can bring up service in backup region in under 15 minutes. Manual Action required. No loss of data, about 30 minutes of downtime.
DynamoDB Global Table maintains replica of MLflow database
Serverless Application can be brought up in backup region within minutes
InfinStor logs code, parameters, and output of cell to MLflow, and captures cell reruns with modifications. Better than simply storing Python code in Git.
Automatic MLflow Tracking of Every JupyterLab Run
Automatic MLflow Tracking of Every Sagemaker Run
Create a bucket for storing MLflow artifacts and provide InfinStor service permission to access it.
Start using our free service by creating a bucket for MLflow artifacts in your AWS account and permitting our service to access the bucket.
MLfLow Kernel connects to an MLflow service and records all the data science activities in the notebook as MLflow artifacts.