AI Dictionary › AI Fundamentals
Batch processing is handling data in lots: requests or records are accumulated and processed all together at a set time, rather than one by one as they arrive. It is the complementary opposite of streaming: where real time favors responsiveness, batch favors efficiency and cost.
A batch job reads a large dataset, processes it start to finish and writes the results, typically during low-load windows such as overnight. The absence of interactivity allows aggressive optimization: parallelizing across many machines, saturating resources, resuming from checkpoints after failures. Frameworks like Apache Spark made distributed batch accessible on petabyte datasets.
In AI, batch is everywhere: training itself proceeds in batches of examples, embedding generation for millions of documents runs as a nightly job, and LLM providers offer discounted batch APIs (often 50% off) for delay-tolerant workloads such as classifying archives or mass-generating summaries. Beyond AI, payroll, billing and bank reconciliation have always been batch.
It is computing's oldest mode: in the 1950s and 60s programs were handed in as decks of punched cards and operators ran them in batches (the word is related to bake, an oven-load) on the mainframe, returning printouts hours later. Operating systems were born precisely to automate this job queue: the General Motors-North American Monitor for the IBM 704, from 1956, is considered among the first.
In Grace's problem solving you learn to decide when it pays to process immediately and when to accumulate and process in bulk: the same batch-versus-streaming choice AI architects face. The scenarios on costs and optimization train you to specify the right mode for each workload.
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