Model Serving Pattern

Author: Manus AI

Date: 2026-02-10

1. Overview

Model serving is the process of deploying a machine learning model to a production environment where it can be used to make predictions. It is a critical step in the machine learning lifecycle, as it allows businesses to leverage their models to create value. There are a variety of different patterns and architectures for model serving, each with its own advantages and disadvantages. The best choice for a particular application will depend on a number of factors, including the specific use case, the required latency and throughput, and the available infrastructure.

This document provides an overview of the most common model serving patterns and architectures. It also includes a comparison of the different approaches and a discussion of best practices.

Common Model Serving Patterns

There are four common patterns for serving machine learning models in production [1]:

• Pipeline: This pattern breaks down a task into a series of steps, with each step being handled by a separate model or a simple processing function. This is a very common pattern, and it is used in a wide variety of applications, such as computer vision and recommendation systems. For example, a computer vision pipeline might consist of a model for object detection, followed by a model for image classification.

• Ensemble: This pattern combines the outputs of multiple models to produce a single prediction. This can be an effective way to improve the accuracy and robustness of a model. There are many different ways to ensemble models, such as averaging their predictions or using a more complex voting scheme.

• Business Logic: This pattern incorporates business rules and logic into the model serving process. This can be useful for a variety of purposes, such as filtering out certain predictions or applying different weights to different models based on the context.

• Online Learning: This pattern allows the model to be updated continuously with new data. This is a powerful technique that can be used to improve the performance of a model over time, especially in dynamic environments where the data is constantly changing.

Model Serving Architectures

There are three main architectures for serving machine learning models [2]:

• Batch Predicting: This is the simplest architecture, where predictions are computed in batch and stored in a database. When a prediction is requested, it is simply retrieved from the database. This approach has high throughput and low latency, but it is not suitable for applications that require real-time predictions or that have unbounded domains.

• Online Synchronous Serving: In this architecture, the model is hosted as a stateless web service. When a prediction is requested, the service computes the prediction and returns it to the user. This approach is more flexible than batch predicting, as it can handle real-time predictions and unbounded domains. However, it can have higher latency than batch predicting, especially for complex models.

• Online Asynchronous Serving: This architecture is similar to online synchronous serving, but the predictions are computed asynchronously. This means that the user does not have to wait for the prediction to be computed before they can continue to use the application. This approach can have lower latency than online synchronous serving, but it is also more complex to implement.

Comparison of Model Serving Architectures

4. Implementation

Here are some best practices for serving machine learning models:

• Choose the right architecture for your application. The best architecture for your application will depend on a number of factors, including the specific use case, the required latency and throughput, and the available infrastructure.
• Use a scalable and programmable serving framework. A good serving framework will make it easy to deploy and manage your models, and it will also provide features for scalability and performance.
• Monitor your models in production. It is important to monitor your models in production to ensure that they are performing as expected. This includes monitoring for things like accuracy, latency, and throughput.
• Use a version control system for your models. A version control system will help you to track changes to your models and to roll back to previous versions if necessary.

8. References

[1] Serving ML Models in Production: Common Patterns

[2] Machine Learning Model Serving Architectures

2. Core Principles

[Content to be added]

3. Key Practices

Key practices for this pattern include careful design, iterative implementation, and continuous monitoring.

5. 7 Pillars Assessment

6. When to Use

This pattern is applicable in distributed systems and platform architectures where the described problem is encountered.

7. Anti-Patterns & Gotchas

Common mistakes include applying this pattern without understanding the specific context and constraints of the system.