Serve existing ML models beside your data: plan the endpoint
Predict serves classic ML models and records predictions with your data systems. Evaluate compatibility, feature freshness and operational behavior before moving an existing endpoint.
Relevant capabilities
- Serving for supported classic ML model formats
- dbt feature models and prediction logs in ClickHouse
- Data-quality monitors
How should you compare the options?
- Is the trained model and its runtime supported?
- How will online features match the training definitions?
- What latency, availability and rollback requirements apply?
What should you bring?
- Model artifact and runtime requirements
- Feature definitions and reference predictions
- Traffic and monitoring requirements
What does a practical first workflow look like?
- Confirm compatibility for one existing model.
- Compare reference predictions and feature inputs in a pilot.
- Exercise rollout, monitoring and rollback before cutover.
What should you check before expanding?
- Check prediction equivalence on reference cases.
- Measure feature freshness and serving latency.
- Validate logging, drift review and rollback ownership.
How should you evaluate the cost?
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.
What needs checking 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.