Inference engine
An inference engine is software that derives outputs or conclusions from inputs by executing a trained model or evaluating facts and rules.
Inside AI
An inference engine is a program that evaluates known facts against rules to derive or modify facts and control an expert system's reasoning loop.
- How it is used
- Executes rule-based reasoning for expert systems and automates decisions or actions within business processes.
- How deep it goes
- Explains the evaluation loop, fact updates, stopping condition, and supporting rule-management components without implementation details.
- Examples
- Uses MYCIN’s infection diagnosis and an industrial emergency-pump rule to demonstrate fact-driven inference.
- What you can do after
- Operate/configure rule-based decision systems by understanding fact evaluation, rule triggering, and maintainable rule separation.
Machine Learning System Design
An inference engine is software that loads a machine-learning model and executes it to produce predictions from inputs.
- How it is used
- Deploys machine-learning models for prediction while balancing execution hardware, latency, cost, and operational constraints.
- How deep it goes
- Explains graph execution, hardware placement, optimization boundaries, and serverless cold starts without deriving engine internals.
- Examples
- Uses CoreML graph splitting, ONNX Runtime optimization, specialized LLM engines, and a quantified serverless cold-start scenario.
- What you can do after
- Evaluate inference engines against hardware placement, model characteristics, optimization needs, latency, scaling, and deployment cost.
Read how Machine Learning System Design explains inference engine →
Natural Language Processing in Action, Second Edition
An inference engine is a computational system that derives conclusions from facts and rules and can use them to support decisions.
- How it is used
- Supports chatbot decisions by combining extracted information with domain knowledge and logical extrapolation.
- How deep it goes
- Defines inference and places it within an NLP pipeline, but stops short of implementation details for the engine itself.
- Examples
- Uses chatbot pipelines, extracted statements, stored statement–response pairs, and edit-distance matching as concrete examples.
- What you can do after
- Evaluate how a chatbot can combine extracted information and domain knowledge to produce logically supported decisions.
Read how Natural Language Processing in Action, Second Edition explains inference engine →
Related concepts
- expert systempart of · An expert system is a computer system that applies explicitly represented domain knowledge and inference procedures to solve problems, draw conclusions, or make recommendations.
- business rules management systempart of · A business rules management system is software for authoring, managing, governing, and executing business rules separately from application code.
- inference frameworkpart of · An inference framework is software infrastructure that supports serving trained machine-learning models and running inference, often with components for model conversion, optimization, and operation.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.