Real-time prediction over streaming data is a pipeline of three separate jobs: transform incoming events into model-ready features, run model inference, and, when needed, store or retrieve the latest feature values. The stream processor, inference runtime, and online feature store are distinct layers; they can be deployed together or separately.
The right design depends on whether your input is append-only or includes updates, where inference runs, how fresh features must be, and whether training and serving use the same feature logic. The official documentation describes several implementation patterns, but does not establish a universally best stack or a cross-platform latency benchmark.
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What happens between a streaming event and a prediction?
- Ingest events. A streaming source supplies records to a stream-processing pipeline. Each record may contain raw event data, identifiers, timestamps, or values that need to be joined or transformed.
- Build model-ready features. The pipeline validates, enriches, aggregates, or otherwise transforms event fields into the input columns the model expects. This work may happen in the stream processor, a feature-processing pipeline, or a remote service; make the placement explicit.
- Run inference. The inference runtime applies a registered or configured model to the selected feature values. A prediction is an output of model inference, not a feature-store lookup.
- Route the result. The pipeline can send prediction fields to a downstream consumer or application. If the design uses an online feature store, that store supplies or retains feature values; it does not replace the stream processor or model runtime.
These stages have different operational concerns. Transformations affect feature correctness and freshness, inference affects model execution and provider behavior, and storage affects how current or historical features can be accessed. A fresh feature value does not by itself guarantee low end-to-end prediction latency.
Which streaming inference patterns are documented?
| Pattern | Where transformations happen | Where inference happens | Key documented characteristics |
|---|---|---|---|
| Flink SQL stream inference | In the Flink stream or SQL pipeline | Through a model registered for Flink SQL | Flink 2.3 documents ML_PREDICT for append-only table input; provider support and execution mode matter. |
| Beam transforms in Dataflow | In Apache Beam transforms, including MLTransform |
Locally or remotely through configured inference, including RunInference |
Google Cloud Dataflow ML documents batch and streaming inference and model refresh while a pipeline runs. |
| Streaming features with an online feature store | In the streaming feature-generation and ingestion path | In the selected inference runtime, with latest feature values retrievable from online storage when needed | SageMaker Feature Store separates latest-value online access from historical offline storage and documents streaming ingestion. |
These are architectural options, not a performance ranking. Their documentation does not provide a controlled comparison of latency, throughput, cost, or scale for a common workload.
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Flink SQL: infer inside the stream
In Flink 2.3, ML_PREDICT applies a registered model to table input and returns the input columns along with prediction columns. The documented call shape is ML_PREDICT(TABLE input_table, MODEL model_name, DESCRIPTOR(feature_columns), ...). Register the model first, and ensure the selected descriptor columns match the model’s input schema.
The Flink 2.3 model-inference documentation supports synchronous or asynchronous inference when the underlying provider supports the selected mode. It recommends considering asynchronous inference for high-throughput scenarios, while noting that performance depends on the underlying model-provider implementation. Async mode is therefore a provider-dependent design choice, not a latency guarantee; check timeout and concurrency configuration along with provider support.
Rank #2
Flink 2.3 documents ML_PREDICT for append-only tables. CDC tables are not supported for this function because inference results are non-deterministic. If your source contains updates or change events, do not assume the SQL inference function will preserve the semantics you need; verify that the input and output behavior fit the application.
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Google Cloud Dataflow ML documents batch and streaming inference, local and remote inference, and preprocessing with Apache Beam’s MLTransform. The documented pipeline pattern combines preprocessing operations with configured model execution through RunInference. Dataflow ML documentation also describes feature enrichment and updating models while a pipeline runs.
These are capabilities to evaluate for a particular Beam transform, runner, model handler, and deployment region, not a blanket claim that every model or refresh method works in every configuration. This pattern places pipeline operation in a managed service rather than requiring the same self-managed operating model as a Flink deployment.
Online and offline features: separate current serving data from history
SageMaker Feature Store supports online, offline, or combined feature groups. Its online store is intended for retrieving latest feature values for real-time predictions; its offline store retains historical feature data for exploration, training, and batch inference. AWS documentation also describes streaming ingestion, with sources such as Amazon MSK and Kinesis as possible inputs to feature generation and ingestion.
Rank #4
Choose this pattern when serving needs a reusable latest-value feature lookup or when historical feature records are needed for training and analysis. Specify how stream updates reach the store and whether transformations occur upstream, in a feature-processing pipeline, or in a service. The store manages feature access and retention; the inference runtime still applies the model.
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How do you keep training features consistent with serving?
Training-serving skew occurs when feature processing differs between model training and live serving. A model may receive values with different definitions, transformations, or timing at prediction time than it saw during training, undermining the usefulness of the output even if inference itself succeeds.
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AWS Feature Store documentation identifies tracking and reusing feature-processing code as a way to reduce this mismatch. In an architecture review, trace each model input back to its transformation, confirm that training and serving implement the same feature definition, and establish which component owns updates. Storing features online and offline does not automatically make the processing logic consistent.
What should you decide before choosing a design?
- Input semantics: Is the source append-only, or does it include updates or CDC? Confirm the selected inference path supports those semantics.
- Inference placement: Will the model run inside the stream pipeline, locally in a managed pipeline, or through a remote endpoint? The choice changes what must be configured and operated.
- Feature access: Does each prediction use features available on the incoming event, or must it retrieve the latest values from an online store? Define how streaming updates reach that store.
- Provider and execution mode: Verify synchronous or asynchronous support for the specific provider and model, plus timeout, buffering, and concurrency behavior.
- Feature consistency: Decide how training and serving share, track, or reproduce the same feature-processing logic.
- Historical requirements: Determine whether feature history is needed for training, exploration, audit, or batch inference; online latest-value access and offline history serve different needs.
- Workload validation: Measure end-to-end behavior with the actual event shape, model provider, feature access, and operational settings. The cited documentation does not support a general latency, throughput, or cost winner.
What do Flink’s feature-engineering figures mean?
Apache Flink’s April 19, 2023 announcement for Flink ML 2.2.0 said the release increased its feature-engineering algorithms from 6 to 33, and that 28 of the 33 were also provided in Spark ML. These are historical, release-specific counts, not current totals for later Flink versions and not evidence that one framework delivers better streaming predictions.
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