mikrobatching

MikroBatching is a technique in data processing and machine learning that groups incoming data into small, fixed-size batches for processing as a unit. It sits between true streaming, which processes records as they arrive, and traditional batch processing, which operates on large data sets at longer intervals. In streaming systems, a micro-batch is formed from data collected over a short window and then processed as a single unit. In machine learning workflows, mikro-batching refers to using small batch sizes to feed computation devices (GPUs, TPUs) to balance latency, throughput, and memory usage.

The approach aims to amortize startup and data-transfer overhead, improve utilization of vectorized computation, and provide more predictable latency. It enables pipeline parallelism by overlapping data collection, preprocessing, and model execution. MikroBatching can also help smooth variabilities in data arrival and processing times, making it easier to maintain steady throughput in real-time systems.

Challenges include increased scheduling and windowing complexity, potential latency variability if window sizes are not well chosen, and diminishing returns if the batch size is not aligned with hardware characteristics. Selecting appropriate micro-batch sizes often requires empirical tuning based on workload, hardware, and latency requirements.

Typical applications are real-time or near-real-time analytics, streaming data pipelines, and online inference systems. In training contexts, mikro-batching is often used as mini-batches to achieve efficient gradient computation within memory constraints, and it can be integrated with streaming architectures that support incremental updates. Related concepts include batch processing, mini-batch gradient descent, and streaming frameworks that implement micro-batching.