// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Batch Inference
Batch inference is when an AI model processes a large group of data inputs all at once, rather than one by one, typically for tasks that don't require immediate results.
TECHNICAL DEFINITION
Batch inference is a machine learning deployment strategy where an AI model processes a large collection of input data points simultaneously, typically offline or on a scheduled basis, to generate predictions, optimizing for throughput and resource efficiency rather than real-time responsiveness.
BACKGROUND
A large language model (LLM) is a neural network trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate and analyze text in many contexts, and are a foundational technology behind modern chatbots. Biased or inaccurate training data can make an LLM's output less reliable.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Offline inference
- bulk prediction
- scheduled inference
USAGE NOTE
Batch inference is suitable for tasks like daily report generation or processing large datasets for analytics.
DEVELOPERS
Organizations developing technology related to Batch Inference.
NVIDIA develops GPUs and software platforms like TensorRT and Triton Inference Server that are critical for accelerating and optimizing batch inference for AI models across various deployment environments.
AWS offers cloud services like Amazon SageMaker, which provides a 'Batch Transform' feature specifically designed for running inference on large datasets in a batch mode, without requiring persistent endpoints.
Google Cloud's Vertex AI platform provides managed services for machine learning, including 'Batch Prediction' capabilities that allow users to run inference on large volumes of data asynchronously.
Azure Machine Learning includes 'Batch Endpoints' that enable users to deploy models for batch inference, processing large quantities of data efficiently and asynchronously.
Databricks provides a unified data and AI platform that enables large-scale machine learning workflows, including the ability to perform high-performance batch inference on vast datasets using Apache Spark and MLflow.
Hugging Face, through its Transformers library and inference solutions, provides tools and techniques for efficiently batching inputs for inference, particularly for large language models and other transformer-based models.
Seldon offers an MLOps platform, Seldon Core, which is designed for deploying machine learning models at scale and supports various inference patterns, including robust batch inference capabilities.
Verta AI provides an MLOps platform that helps enterprises manage, deploy, monitor, and improve ML models, including supporting efficient batch inference workflows for various use cases.