Batch inference
Batch inference is the process of applying a trained machine-learning model to multiple collected inputs together rather than processing each input individually on demand.
Machine Learning System Design
The process of applying a machine learning model to a collected batch of inputs rather than processing each input continuously.
- How it is used
- Deploys predictions for scheduled or on-demand workloads where throughput and cost outweigh per-request latency.
- How deep it goes
- Explains scheduling, scaling, monitoring, reruns, and profiling, but stops short of implementation code or model internals.
- Examples
- Uses photo-index generation and scheduled retail demand forecasting as concrete batch workloads.
- What you can do after
- Operate scalable scheduled prediction jobs with monitoring, durable outputs, and recovery through reruns.
Read how Machine Learning System Design explains batch inference →
Effective Data Analysis
Batch prediction generates predictions for grouped records at scheduled intervals rather than individually on demand.
- How it is used
- Delivers model outputs to stakeholders when grouped, scheduled predictions are sufficient for planning or operational decisions.
- How deep it goes
- Defines the workflow, compares it with live prediction, and identifies suitable use cases without detailing production implementation.
- Examples
- Uses customer churn, daily sales, supply-chain problems, and spreadsheet-based prediction outputs as examples.
- What you can do after
- Apply it via a spreadsheet or stored-data workflow to produce grouped predictions for non-real-time decisions.
Machine Learning Platform Engineering
Batch inference is the process of applying a trained machine-learning model to a finite collection of inputs in grouped execution rather than one request at a time.
- How it is used
- Automates repeatable model scoring over finite datasets within an orchestrated ML workflow.
- How deep it goes
- Traces data loading, feature retrieval, model execution, output writing, compilation, and pipeline execution, but defers serving implementation.
- Examples
- Uses an income-classification pipeline with MinIO inputs and outputs, feature retrieval, staged model selection, and compiled KFP YAML.
- What you can do after
- Apply it via Kubeflow Pipelines to build, compile, schedule, and track reproducible dataset-scoring workflows.
Read how Machine Learning Platform Engineering explains batch inference →
Related concepts
- BentoMLused by · BentoML is an open-source framework for packaging, deploying, and serving machine-learning models as production services or APIs.
- language modelalternative to · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
- inferencekind of · Inference is the process of applying a trained model to input data to produce outputs or predictions using its learned parameters.
- generationcontrasts with · Generation is the computational production of outputs from supplied inputs or context, often by applying a model to produce predictions or new content.
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