Batch Inference and Offline Scoring
Batch inference applies a trained machine learning model to a fixed collection of records and writes the predictions for later use. It favors throughput, repeatability, and cost control over an immediate response for each record.
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Intro
Batch Inference and Offline Scoring
Batch inference applies a fixed model version to a bounded data set and stores the resulting predictions. The consumer reads those predictions later. Unlike an online endpoint, the scoring system does not have to answer inside an interactive request. The primary objective is usually to finish an entire run by a deadline while controlling cost and preserving a traceable result.
Typical uses include a nightly churn score for every customer, a weekly demand forecast for every product, embeddings for a document collection, and a backfill after a model correction. Batch inference also supports model evaluation because the same input snapshot can be scored with several model versions.
Architecture and data flow
A production run connects six concerns:
trigger -> input snapshot -> feature transform -> model workers -> output table
| | |
+-- data identity +-- model identity +-- run metadata
The trigger can be a schedule, an upstream data event, or a manual backfill. It binds the run to an input partition, table snapshot, or object prefix. Feature transformation converts source fields into the schema expected by the model. Workers load the model and process records in mini-batches. The publication step writes predictions and identifiers to durable storage. Run metadata links the output to the model version, feature logic, input identity, and execution time.
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Sources
- https://docs.ray.io/en/latest/data/batch_inference.html
Supports
- Offline batch inference generates predictions for a fixed input data set
- Ray Data loads data, initializes model workers, maps prediction over batches, and writes results
- Batch size trades vectorized throughput and GPU utilization against memory use
- Job-level checkpointing can skip already processed records after failure
- https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html
Supports
- Batch Transform scores large stored data sets without a persistent endpoint
- Input objects are distributed by key across instances
- Supported inputs can be split into mini-batches
- Predictions preserve record order within corresponding output files
- https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html
Supports
- Batch Transform can join selected input fields with prediction output
- Explicit input identifiers can be retained with prediction results
- https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-errors.html
Supports
- Payload limits and unsplittable records can fail a batch transform job
- Incomplete multipart uploads can remain after some failures
- https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-model-deployments?view=azureml-api-2
Supports
- Batch endpoints run inference over large data volumes when low latency is not required
- Invoking an endpoint starts a batch scoring job over stored inputs
- Deployments process inputs in mini-batches and store outputs
- https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-data-batch-endpoints-jobs?view=azureml-api-2
Supports
- Batch endpoint inputs point to stored data rather than carrying the full data in the request
- Batch jobs expose named inputs, outputs, and job identity
- https://cloud.google.com/vertex-ai/docs/predictions/get-batch-predictions
Supports
- Vertex AI creates asynchronous batch prediction jobs from stored input
- Batch prediction jobs write results to a configured output destination
- https://mlflow.org/docs/latest/ml/model/index.html
Supports
- An MLflow Model packages an artifact in flavors understood by downstream tools
- Python-function models can be used for batch inference through an Apache Spark user-defined function
- https://docs.feast.dev/untitled/getting-started/concepts/point-in-time-joins
Supports
- A point-in-time join retrieves feature values available at each entity timestamp
- The feature lookback window is relative to the entity timestamp rather than job execution time
- https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html
Supports
- A directed acyclic graph defines tasks, dependencies, schedule, and run behavior
- https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/backfill.html
Supports
- Backfills create runs across a historical date range
- Backfill controls include reprocessing behavior, concurrency, ordering, and dry-run preview
- https://airflow.apache.org/docs/apache-airflow/stable/best-practices.html
Supports
- Retryable tasks should produce the same outcome on rerun
- Upserts can avoid duplicate records caused by repeated inserts
- Tasks should use fixed partitions instead of changing latest data
- https://docs.snowflake.com/en/developer-guide/snowflake-ml/inference/batch-inference-jobs
Supports
- Snowflake batch inference runs asynchronous inference over static or periodically updated data
- Jobs use registered models, stored inputs and outputs, distributed compute, and job management operations
- Job compute can wind down after inference completes
- https://docs.snowflake.com/developer-guide/snowflake-ml/model-registry/warehouse
Supports
- Snowflake can invoke registered models for native batch inference from SQL or Python
- Warehouse and container-service runtimes fit different model size and hardware requirements
- https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview
Supports
- Snowflake Model Registry stores model versions, metrics, and metadata
- Snowflake can run distributed inference and monitor model performance and drift
- https://docs.bentoml.com/en/latest/scale-with-bentocloud/deployment/batch-inference-jobs.html
Supports
- BentoML and BentoCloud run one-time or recurring batch inference jobs
- On-demand deployments can terminate after a batch job
- Batch jobs can use dedicated inference hardware and automatic scaling
- https://docs.databricks.com/aws/en/mlflow/models
Supports
- Databricks can load a registered MLflow model and score a Spark DataFrame through a user-defined function
- Model dependencies must match the inference environment
- https://github.com/sindresorhus/awesome
Supports
- The central Awesome index links curated machine learning and software engineering lists
- https://github.com/EthicalML/awesome-production-machine-learning
Supports
- The list curates Ray, MLflow, Feast, and Apache Airflow as production machine learning ecosystem projects
- https://docs.databricks.com/aws/en/machine-learning/model-serving
Supports
- Databricks provides governed model deployment and batch inference interfaces