🤖 AI / ML

Model Inference

model inference · inference · prediction · scoring · online inference · batch inference

Definition

Inference is the application of a trained model to new input data to obtain predictions without changing its parameters; it runs in production, in real time.

Inference: from training to prediction

An ML model goes through two fundamentally different phases. Training — the model learns patterns from historical data and fits its parameters. Inference — the finished model is applied to new data to produce predictions.

Only inference runs in production. A shopper opens a category page, the system triggers inference on the recommender model, and within tens of milliseconds a personalized product list comes back.

Inference requirements in e-commerce

Online inference works under hard time constraints:

Scenario	Acceptable latency	Reason
Recommendations on a product page	< 50–100 ms	Blocks page rendering
Category personalization	< 100 ms	Loads together with the product list
AI shopping assistant	< 2–3 s	The shopper is waiting for an answer
Batch scoring of segments	No constraint	Offline processing

Breaching the latency budget in the first two scenarios means a fallback — showing default, non-personalized recommendations — or a rendering delay, and both affect conversion.

Online versus batch inference

Online inference is synchronous: a request arrives, the model runs, the answer returns. It serves personalization, recommendations and real-time search, and it needs highly available inference infrastructure.

Batch inference is asynchronous: the model periodically processes large volumes of data and writes the results to a database. When a page is requested, the system reads the precomputed result — fast, but the data may be stale.

In practice recommender systems usually combine both: batch for long-term preferences (an affinity profile recomputed every few hours) plus online for short-term signals from the current session.

Tip: cache inference results for the standard scenarios such as bestsellers and trending. That cuts load on the inference server by 60 to 80% without sacrificing quality for most shoppers.

LLM inference: a special case

For large language models, inference is an order of magnitude more resource-hungry than for classical ML models. A single token in the answer of a GPT-class model takes gigaflop-scale computation. Hence:

Dedicated GPU or TPU servers for inference
Quantisation — lowering numeric precision for speed with little quality loss
Streaming output — tokens are sent to the user as they are generated, instead of waiting for the full answer
KV-cache reuse to speed up repeated context

Frequently asked questions

How is inference different from training a model?

Training is the iterative process of fitting a model's parameters on historical data; it can take hours or days and happens periodically. Inference is a single application of the already trained model to a new request, and for online scenarios it has to fit inside milliseconds.

What is inference latency?

The time from receiving a request to returning a prediction. For e-commerce recommender systems it is a critical metric: a delay above 100 to 150 ms starts to affect conversion. For an LLM assistant's answer, seconds are acceptable — the shopper expects the model to think.

Which methods speed inference up?

Quantisation (replacing float32 with int8 cuts model size by a factor of four), distillation (training a small model to imitate a large one), batching (processing several requests together), caching results for repeated inputs, and using specialised accelerators such as GPUs or TPUs.

What is batch inference versus online inference?

Online, or real-time, inference produces a prediction for a single request as it happens; this is what serves recommendations when a page opens. Batch inference processes data in bulk on a schedule and is used to precompute recommendations or scores for the whole customer base.