// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Inference Pipeline
An inference pipeline consists of the automated steps involved in using a trained model to make predictions on new data.
TECHNICAL DEFINITION
An Inference Pipeline processes new input data through a deployed machine learning model to generate predictions or classifications, often including pre-processing steps and post-processing logic before returning results.
BACKGROUND
Prompt engineering is the process of structuring natural language inputs to produce specified outputs from a generative AI model. Context engineering is the related area of software engineering that focuses on the management of non-prompt and prompt contexts supplied to the GenAI model, such as system instructions, metadata, API tools and tokens.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Prediction pipeline
- scoring pipeline
- real-time inference
USAGE NOTE
Inference pipelines are optimized for low latency and high throughput in production.
DEVELOPERS
Organizations developing technology related to Inference Pipeline.
Develops GPUs and software like Triton Inference Server, which is critical for high-performance AI inference pipelines, enabling efficient deployment and execution of models at scale.
Provides a comprehensive cloud platform for building, training, and deploying ML models, including managed inference endpoints and MLOps tools for robust and scalable inference pipelines.
Offers an end-to-end platform for machine learning, including robust model deployment and serving infrastructure for efficient AI inference, supporting various model types and scales.
Provides a cloud-based environment for building, deploying, and managing ML solutions, featuring tools for scalable AI inference pipelines, MLOps, and prompt engineering integration.
Offers a platform for deploying and serving transformer models, including dedicated inference APIs and open-source tools (like Text Generation Inference) for building efficient inference pipelines for large language models.
Provides an MLOps platform that includes capabilities for deploying and managing machine learning models in production, facilitating scalable inference pipelines with strong data integration.
Develops a framework for building applications with large language models, providing tools for prompt management, chaining, and connecting to various inference endpoints, essential for prompt design and orchestration within inference pipelines.
Offers an MLOps platform for tracking, visualizing, and managing machine learning experiments and models in production, including tools for monitoring and optimizing the performance of inference pipelines.