Online inference is the process of using a trained machine learning model to make predictions in real time as new data arrives. Unlike offline inference, where predictions are made in batches on a static dataset, online inference is all about speed and responsiveness. The model is deployed in a way that lets it receive individual data points (like a user query, a sensor reading, or a transaction) and instantly output a prediction or decision, often powering interactive applications and services.
You encounter online inference every day, even if you don’t realize it. When you use a voice assistant, type a search query, get a recommendation on a streaming service, or even see a fraud alert from your bank, there’s likely a model running online inference behind the scenes. The key requirement is low latency—the time between sending input to the model and receiving the output should be minimal, sometimes on the order of milliseconds.
Technically, online inference involves serving a model through a dedicated system or API that can handle requests as they come in. This system might be integrated into a web server, an edge device, or a cloud-based service. The main focus is on minimizing inference time, ensuring reliability, and handling variable loads. In many cases, optimizations like model quantization, batching (processing small groups of requests together for efficiency), and hardware acceleration (using GPUs, TPUs, or custom chips) are used to achieve the required level of performance.
Another important aspect is scalability. Since real-world applications can have unpredictable traffic—think about a viral app or a sudden spike in e-commerce activity—online inference systems need to dynamically scale up or down. This often involves orchestration tools and load balancers that distribute incoming requests across multiple model instances.
It’s worth noting that online inference is different from online learning or online training. In online inference, the model parameters stay fixed; the model is not updated or retrained with new data in real time. The focus is purely on prediction, using a model that was previously trained and validated. If the model needs to be updated, this typically happens in a separate process, and the new model is deployed when ready.
Finally, online inference is essential for applications where quick feedback is crucial. In autonomous vehicles, healthcare monitoring, financial trading, and many IoT (Internet of Things) scenarios, the ability to react instantly to new information can be critical. Building robust online inference pipelines is thus a cornerstone of deploying AI in production.
