// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Batch Processing
Batch processing involves collecting and processing data in large groups or "batches" at scheduled intervals, rather than individually or continuously. It's like doing all your laundry once a week instead of washing each item as it gets dirty.
TECHNICAL DEFINITION
Batch processing is a data processing method where large volumes of data are collected over a period, stored, and then processed in discrete, scheduled runs, typically for tasks like nightly reports, payroll, or historical data analysis, often leveraging systems like Hadoop MapReduce or Spark.
BACKGROUND
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences, which can then be used to train other models through reinforcement learning.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Offline Processing
- Scheduled Processing
- Bulk Processing
- Data Batching
USAGE NOTE
Batch processing is efficient for large-scale, non-urgent data tasks and historical analysis.
DEVELOPERS
Organizations developing technology related to Batch Processing.
Offers a dedicated Batch API for processing large numbers of non-latency-sensitive API requests asynchronously. This is designed for tasks like summarization or classification on large datasets, reducing costs and improving rate limits compared to one-by-one requests.
Provides Batch Prediction services within its Vertex AI platform. This allows users to get inferences for an entire dataset at once, a core feature for applying AI models, including LLMs like Gemini, to large-scale data processing tasks.
Offers Batch Transform jobs in Amazon SageMaker, a feature specifically designed to run predictions on large datasets without needing to manage a persistent endpoint. This is fundamental for production AI workflows involving large volumes of data.
Provides batch endpoints, which allow users to deploy models for long-running, asynchronous inference on large volumes of data. This service is designed to be a reliable and scalable way to operationalize models that process data in batches.
The Databricks platform is built for large-scale data engineering and integrates AI/ML capabilities, including LLMs. It excels at applying model inference as a step in a batch data pipeline using Spark, processing terabytes of data efficiently.
Develops and supports the Ray framework, an open-source standard for distributed computing. A primary use case for Ray is large-scale batch inference, where models are run in parallel over massive datasets for high-throughput processing.
While known for its model hub, Hugging Face's libraries like `transformers` and `datasets` are foundational for building custom batch processing pipelines. Its Inference Endpoints and services also support running models over large batches of prompts.
Specializes in data for AI development, including generation and annotation. Their platform inherently uses batch processing to run foundation models over vast datasets to generate, pre-label, and validate data for training and evaluation.