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Start with the decision, not the dataset
Before selecting data or infrastructure, specify what the system is predicting, what action the prediction may trigger, and how success will be measured. Also set the decision deadline: a recommendation needed within a few seconds has different operating constraints from a result that can wait for a scheduled update. Databricks advises aligning on what the model needs to do and how its performance will be assessed before building it. See its machine-learning lifecycle guidance.
These choices determine what information is relevant, how recent it must be, and what happens if it is missing or late. They also shape the measures to monitor, such as prediction quality, data freshness, serving latency, and throughput.
What data should be available when a decision is made?
At serving time, the application needs the request or event being scored and the inputs used to describe the relevant entity or state. Depending on the use case, those inputs may come from current events, reference data, or context supplied with the request. They should use a stable identifier where one is needed to retrieve the right entity’s information, and a feature representation that matches the deployed model’s expected input schema.
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- Entity or request identity: a consistent identifier that allows the system to look up the correct record or state.
- Event time: when an event actually happened, so the system can order events and assess recency.
- Availability time: when the information became available to the system. Keeping this distinct from event time helps establish what the system could have known at a given decision point.
- Decision-relevant features: values derived from applicable events, reference information, or request context and prepared in the format expected by the model.
- Input handling rules: defined behavior for missing, late, stale, contradictory, or invalid values. The right fallback depends on the application; there is no universal policy.
A feature store is one way to manage and serve such data, but the underlying needs do not require a product called a feature store. AWS describes online and offline feature-store patterns, including records with identifiers and event times, in its SageMaker Feature Store documentation.
How fresh must the data be?
Set the freshness budget from the pace of the events and the cost of acting on outdated information. Freshness means the end-to-end delay between an event occurring and its updated feature being available for retrieval. That is different from inference serving latency: the time taken to retrieve inputs and return a prediction after a request arrives. A system can answer quickly using stale features, or use fresh features but miss its deadline if retrieval and inference take too long.
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There is no general threshold that applies to every real-time AI decision. A fraud signal, inventory state, or customer preference may change at different rates and carry different consequences when out of date. Define acceptable lag and the end-to-end decision deadline for the actual use case, then monitor them separately.
Snowflake documents service-specific performance figures for its Online Feature Store: 10 ms p50 REST query-serving latency and under 2 seconds of end-to-end freshness with its stream-ingestion path, according to documentation accessed in 2026. These are product claims for that service and configuration, not targets or benchmarks for AI systems generally. Snowflake’s online feature-store documentation is marked as preview; check its current status and requirements before relying on it.
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How should live and historical data be organized?
Many implementations separate the fast serving path from the history needed to build and assess models. An online store commonly provides current feature values for inference; an offline store keeps historical records for exploration, training, and batch work. Consistent feature definitions and transformations across these paths help reduce training-serving skew—the risk that a model is trained on data prepared differently from the inputs it receives after deployment.
This is a common documented pattern, not a mandatory architecture. AWS describes online and offline storage in its SageMaker Feature Store guidance, and Snowflake documents synchronization and real-time feature-view patterns in its Online Feature Store documentation.
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Which data-update approach fits the freshness budget?
Choose the update path by comparing the required freshness and request deadline with throughput, historical-data needs, operational complexity, and access controls. The documented options have different trade-offs; no single one is best for every use case.
| Approach | When it may fit | What to account for |
|---|---|---|
| Batch or scheduled refresh | When updates can wait for a configured schedule. | Suitability depends on how much staleness the decision can tolerate. Snowflake documents configurable offline-to-online synchronization lag, and AWS supports batch feature ingestion. Snowflake; AWS. |
| Streaming updates | When incoming events should update features before a later live inference request. | Freshness depends on the service and configuration. AWS documents stream sources feeding online features; Google Cloud describes streaming ingestion that makes feature values available for online serving within seconds in its service context. AWS; Google Cloud. |
| Request-time computation | When a feature can be calculated from the current request and upstream values as the query arrives. | Include the computation and upstream retrieval time in the end-to-end deadline. Snowflake documents this as a real-time feature-view pattern. Snowflake. |
| Online plus offline storage | When the system needs current values for serving as well as historical records for training, exploration, or batch work. | Keep definitions and transformations aligned across the paths; this is a common pattern rather than a requirement to use a feature-store product. AWS; Snowflake. |
What data is needed to train and evaluate the model?
Live inference inputs are only part of the data requirement. Training and evaluation need historical examples that connect the relevant features to outcomes or labels suited to the prediction target. To assess a decision honestly, retain timestamps or equivalent information that establishes what would have been available when each historical decision was made.
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- Set aside valid test data and keep modeling choices separate from it. Databricks recommends deciding early how test data will be verified and warns against making modeling decisions based on the test set in its lifecycle guidance.
- Check coverage, missing values, outliers, skew, measurement accuracy, relevance, and whether the examples represent the intended population and operating context.
- Review potential bias and whether the data is sufficient for the target pattern. A large dataset is not automatically suitable if it omits important cases or does not reflect the intended use.
- Preserve historical feature records where needed to support exploration, training, and evaluation. AWS distinguishes an online store’s latest records from the offline store’s historical record in its feature-store documentation.
What should be monitored and governed?
During operation, monitor data freshness and quality alongside latency, throughput, and model performance against the requirements of the use case. Keep track of data sources, feature definitions, versions, and relevant transformations so that changes can be understood and investigated.
When decisions affect people, consider what information is needed to explain the data and reasoning behind an outcome, what audit records are appropriate, and whether people need a route to review. The UK Information Commissioner’s Office discusses what may go into an explanation of an AI-assisted decision in its explanation guidance. The UK Government’s Data and AI Ethics Framework addresses responsible data and AI practice. These are UK sources; applicable requirements depend on jurisdiction, domain, and the effects of the decision.
Databricks’ lifecycle guidance captures the priority in a practical way: “Before building anything, align on what the model needs to do and how you will know it is working.”
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