HyperLake — Serve existing ML models beside your data: plan the endpoint
FREE PLANNING CHECKLIST

Predict serves classic ML models and records predictions with your data systems. Evaluate compatibility, feature freshness and operational behavior before moving an existing endpoint.

Prepare these inputs
[ ] Model artifact and runtime requirements
[ ] Feature definitions and reference predictions
[ ] Traffic and monitoring requirements

Your first workflow
[ ] Confirm compatibility for one existing model.
[ ] Compare reference predictions and feature inputs in a pilot.
[ ] Exercise rollout, monitoring and rollback before cutover.

Acceptance checks
[ ] Check prediction equivalence on reference cases.
[ ] Measure feature freshness and serving latency.
[ ] Validate logging, drift review and rollback ownership.

Decision criteria
[ ] Is the trained model and its runtime supported?
[ ] How will online features match the training definitions?
[ ] What latency, availability and rollback requirements apply?

Cost and scope
Compare the cloud bill, platform charges, operating effort and migration work for the same workload. Cloud capacity is not free. Charges and scope need agreement before paid deployment.

Before switching
AWS and Google Cloud are available today; other deployment environments are roadmap. Preserve the existing workload and access path until compatibility, recovery and cutover checks pass.

Sources
https://hyperlake.cloud/stacks
https://hyperlake.cloud/faq
https://hyperlake.cloud/get-started

Next step
No free cloud capacity is promised. Deployment scope and charges are agreed separately; AWS and Google Cloud are available today.
https://hyperlake.cloud/solutions/classic-ml-serving

This is a planning template, not a completed assessment or a quote. No personal data is required to download it.