What is Batch Inference?

What Is Batch Inference?

Batch Inference is the practice of grouping multiple AI prediction requests together and processing them as a single batch rather than handling each request individually. Instead of sending one document, one image, or one data record through your AI model at a time, you collect hundreds or thousands of items and process them all at once.

This approach is similar to how a commercial laundry service operates. Rather than washing one shirt at a time, they collect a full load and process everything together, which is far more efficient in terms of time, water, and energy per garment.

Batch Inference is the counterpart to real-time or online inference, where each request is processed immediately as it arrives. Both approaches have their place, and the choice between them depends on whether your use case requires instant responses or can tolerate a delay.

How Batch Inference Works

The Batch Inference workflow typically follows these steps:

1. Data collection: Prediction requests are accumulated over a period of time or until a certain quantity is reached. This might be all customer records that need a credit score update, all product images that need classification, or all documents that need sentiment analysis.
2. Batch assembly: The collected data is formatted and organised into a batch that the AI model can process efficiently.
3. Parallel processing: The batch is sent to the AI model, which processes all items simultaneously or in rapid succession, taking advantage of GPU parallel processing capabilities.
4. Result storage: Predictions are stored in a database, data warehouse, or file system for downstream use by other applications or reports.
5. Scheduling: Batch jobs are typically scheduled to run at regular intervals, such as hourly, nightly, or weekly, depending on business requirements.

Modern batch inference systems can process millions of predictions in a single run. Cloud providers like AWS, Google Cloud, and Azure all offer managed batch inference services that handle the infrastructure automatically.

Why Batch Inference Matters for Business

For businesses in Southeast Asia, Batch Inference offers compelling advantages for many common AI use cases:

• Dramatically lower costs: Processing predictions in batches is significantly cheaper than real-time inference. GPU utilisation is much higher because the hardware processes a continuous stream of data rather than sitting idle between individual requests. Depending on the workload, batch processing can reduce per-prediction costs by 50-80% compared to real-time inference.

• Higher throughput: Batch processing can generate millions of predictions per hour, which would be impractical to achieve through individual real-time requests. This is essential for applications that need to score or classify large datasets regularly.

• Simpler infrastructure: Batch inference systems do not need to maintain always-on servers waiting for requests. Resources can be provisioned when the batch job starts and released when it completes, avoiding the cost of idle infrastructure.

• Predictable scheduling: Batch jobs run on a schedule, making resource planning and cost forecasting straightforward. You know exactly when the jobs will run and approximately how long they will take.

Common Batch Inference Use Cases

Many AI applications are naturally suited to batch processing:

• Customer segmentation: Updating customer segments and personalisation scores nightly based on the latest behavioural data. A retail company in Southeast Asia might re-score all customers every night to update targeted marketing campaigns.
• Fraud screening: Processing all daily transactions through fraud detection models during off-peak hours, flagging suspicious activity for review the next morning.
• Document processing: Classifying, extracting information from, or summarising large volumes of documents such as insurance claims, loan applications, or compliance reports.
• Recommendation pre-computation: Generating product or content recommendations for all active users in advance, so the recommendations are ready instantly when users visit the platform.
• Data enrichment: Running AI models to enrich customer records, product catalogues, or other business data with predicted attributes, sentiment scores, or classifications.

Implementing Batch Inference

For organisations setting up Batch Inference:

1. Identify workloads where a delay of minutes to hours is acceptable. If users need instant responses, real-time inference is necessary. If results can be pre-computed and used later, batch is the better choice.
2. Choose a scheduling tool such as Apache Airflow, AWS Step Functions, or Google Cloud Workflows to orchestrate batch jobs reliably.
3. Optimise batch sizes for your hardware. Larger batches generally improve GPU utilisation, but excessively large batches may cause memory issues. Test to find the optimal batch size for your model and infrastructure.
4. Implement monitoring for batch job health, including job completion times, error rates, and output quality checks.
5. Design for failure recovery by building checkpointing into long-running batch jobs. If a job fails midway through processing a million records, you want to resume from where it stopped rather than starting over.

Batch Inference is often the most cost-effective way to deploy AI at scale. For businesses processing large volumes of data regularly, it should be the default approach, with real-time inference reserved for use cases that genuinely require immediate responses.