What is AI inference?

AI inference in machine learning means the process of predicting an outcome by a trained AI model based on new data. To clarify the AI inference meaning, it is the next stage after training an AI model, and it's the moment of truth when the model uses all the knowledge it has previously acquired to process unseen data.

For example, a spam detection model is trained on massive datasets with spam and non-spam emails. At the inference stage, the AI model is expected to predict whether a new email is spam by analyzing and comparing email characteristics against patterns learned during training.

AI inference vs training vs fine-tuning

An AI model's lifecycle includes three core stages, except for data collection:

Training includes feeding an AI model with data to make it form relationships within the training set and encode these relationships in model weights – numerical parameters that define the model's behavior. 2.Fine-tuning is adapting an AI model trained on vast datasets to smaller ones for specific tasks to cut time and effort for training a new model. Learn the difference between RAG, fine-tuning, and prompt engineering.
AI inference is the stage, where the pre-trained and fine-tuned model makes predictions based on real-time data.

After a successful inference stage, the model is ready for deployment, which includes setting up API endpoints and configuring the infrastructure for handling an intensive flow of requests.

Types of AI inference

AI inference can be processed in the cloud for more power and better scaling or on the edge for faster operation and privacy.

Cloud inference has two modes:

Real-time inference is performed within milliseconds, which is crucial for interactive apps.
Batch inference implies periodic analysis of large volumes of data.

Edge inference is performed on a device, be it a personal computer or even a smartphone, which ensures lower latency, costs, and offline functionality.

How does AI inference work?

To clarify the process behind AI inference, there are three main steps:

Input data preparation means that data, such as an image or an email, is prepared to match the format and characteristics of the data used in prior training.
Execution involves analysis of the input data by comparing it against patterns specific to the data learned during training.
Output generation is the process of a model predicting the most probable result, such as estimating a 95% likelihood that the given image contains a cat, in computer vision.

What does AI inference mean in terms of computational resources? Even though the model training is far more demanding than inference, when it comes to processing millions of requests, AI inference becomes the most resource-intensive part that requires powerful hardware (TPUs and GPUs) and techniques for optimizing data processing.

The best practices for optimizing AI inference

AI developers strive to optimize computational costs, speed, and efficiency of making predictions, following the next practices.

Pruning – removing unnecessary parts of the model to reduce its size
Quantization – reducing the precision of the model's weights
Weight sharing – distribution of weights across multiple layers
Knowledge distillation – transferring knowledge from a teacher to a student model
Low-rank factorization – decomposing large matrices into smaller ones
Caching – storing intermediate inference results for faster retrieval

These and other techniques help reduce load on different stages, eventually resulting in lower resources for AI inference.

Applications of AI inference

Almost every practical AI application relies on inference as the development stage. The most popular applications include:

Natural language processing
Generative AI and LLMs
Image and video recognition
Recommendation engines
Autonomous vehicles and robotics
Healthcare diagnosis
Financial services and fraud detection