⚡Inference

NEW

Also known as: Forward pass

The process of using a trained model to generate predictions for new, unseen data.

DESCRIPTION

Inference executes a trained model to produce outputs—predictions, classifications, or embeddings—from new inputs. Production inference emphasizes latency, throughput, and cost via optimizations like batching, quantization, and hardware acceleration. It includes online (real-time) and batch modes, as well as edge deployments. Monitoring accuracy and drift during inference is crucial for reliability and compliance.

ALTERNATIVE DEFINITIONS

NVIDIA

AI inference is the process of using a trained AI model to make predictions on new data.

Clear operational focus on using trained models for predictions.

Source

Microsoft Learn

In machine learning, inference refers to the process of using a trained model to make predictions.

Concise definition framed in Azure Machine Learning context.

Source

TensorFlow

Inference is the process of sending new data to a trained model to obtain predictions.

Places inference within serving architecture for production use.

Source

RELATED TERMS

Model Serving Delays Throughput Batch Inference Online Inference Edge Inference Quantization GPU Acceleration A/B Testing

COMPETING TERMS

Training

RELATIONS

• Narrower: Batch Inference Bayesian Inference Causal Inference Frequentist Inference GPU Inference LLM Inference Membership Inference Online Inference Real-time Inference Statistical Inference Variational Inference

KNOWLEDGE AREAS

AI MLOps Data Science

TARGET AUDIENCE

AI Engineer MLOps Engineer Data Scientist Software Developer Cloud Architect

EXTERNAL SOURCES

• Wikipedia - Prediction#Machine learning
• NVIDIA TensorRT - High-performance inference optimization
• TensorFlow Serving - Serving models for inference
• TorchServe - Model serving for PyTorch inference

TOP QUESTIONS

What is the difference between training and Inference?

Training updates model parameters using labeled data and optimization. Inference uses the fixed model to make predictions on new inputs, prioritizing latency, throughput, and cost.

Category: Misunderstanding

What are common Inference deployment patterns?

Online (real-time) APIs for low-latency responses, batch scoring for throughput, and edge inference for on-device processing are common patterns.

Category: Example

How can I reduce Inference latency?

Use model compression (quantization, pruning), optimized runtimes (TensorRT, ONNX Runtime), hardware acceleration (GPUs, NPUs), and request batching where applicable.

Category: HowTo

What is model drift during Inference?

Drift occurs when data distribution or relationships change, degrading predictions. Monitor features and performance, retrain or recalibrate as necessary.

Category: Risk

What is the risk of ignoring Inference monitoring?

Without monitoring, silent failures erode trust and cause business impact. Set up metrics, alerts, and shadow deployments to detect issues early.

Category: Risk

How does batching affect Inference?

Batching improves throughput and utilization but increases per-request latency. Tune batch sizes to application SLA and hardware capabilities.

Category: Opinion

What is cold start in Inference services?

Cold starts occur when instances spin up to handle requests, causing initial latency. Use warm pools, provisioned concurrency, or autoscaling strategies.

Category: Requirements

How does ONNX help with Inference portability?

ONNX provides a common model format allowing deployment across frameworks and runtimes, simplifying optimization and hardware selection.

Category: Example

What metrics matter for Inference?

Latency percentiles, throughput (QPS), error rate, resource utilization, and business KPIs are key. Track model performance metrics in parallel.

Category: Management