# ai-platform-engineering

Published articles for ai-platform-engineering.

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## Creating an AI Platform for classic ML online inference

DevFeed: [Creating an AI Platform for classic ML online inference](<https://devfeed.tech/articles/creating-an-ai-platform-for-classic-ml-online-inference-22589.md>)

Original publisher: [Read original article](<https://medium.com/amex-gbt-technology/creating-an-ai-platform-for-classic-ml-online-inference-e2165d68e18a?source=rss----60a0578f4096---4>)

Author: Rohith Leeladharan

Published: 2026-09-10T07:26:46Z

Content type: tutorial

Language: en

Sources: [Amex GBT Technology](<https://devfeed.tech/sources/amex-gbt-technology.md>)

Topics: [AI Platform](<https://devfeed.tech/topics/ai-platform.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [MLOps](<https://devfeed.tech/topics/mlops.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-platform](<https://devfeed.tech/tags/ai-platform.md>), [ai-platform-engineering](<https://devfeed.tech/tags/ai-platform-engineering.md>), [deploy](<https://devfeed.tech/tags/deploy.md>), [experiments](<https://devfeed.tech/tags/experiments.md>), [feature-store](<https://devfeed.tech/tags/feature-store.md>), [inference](<https://devfeed.tech/tags/inference.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [ml](<https://devfeed.tech/tags/ml.md>), [mlops](<https://devfeed.tech/tags/mlops.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [predictions](<https://devfeed.tech/tags/predictions.md>)

### AI overview

This article describes how American Express Global Business Travel built an AI platform for deploying classic machine-learning systems and supporting online inference. It explains the platform's requirements--simplicity, self-service, experimentation, and continuous improvement--and details the pre-process, predict, post-process pattern used by inference engines.

### Source excerpt

Introduction In 2021, we were given the mission to have AI Systems running in production. The team, instead of just following a classical MLOps process, that involves transforming a Jupyter notebook into a product running in production, decided to go further by creating a platform to deploy AI systems in production. The team decided the platform should respect these requirements: Simplicity: The code powering AI systems should be simple, readable, and easy to maintain -- less intricacy means fewer bugs in production and greater reliability. Self-service: Anyone should be able to build and deploy AI systems autonomously, without depending on a central team. Experimentation: The platform should make it easy to run and iterate on experiments. Continuous improvement: Data related to events and interactions within AI systems must be captured, enabling monitoring and continuous improvement over time. In this article, we will walk through the work done to build a platform that fulfills these four requirements. Background At American Express Global Business Travel, we use machine learning (ML) models for a variety of user experiences like ranking hotel and flight search results. Our ML models are wrapped in inference engines that handle both pre-processing of input data before we run a prediction with the model, and post-processing of output data before returning the output to the caller. The overall flow looks something like this: Figure 1: Handling an inference request A client service that would like the ML model's predictions provides necessary context about the request like which user the request is for. Then, optionally, the inference engine fetches any necessary features for inference from our feature store [part 1][part 2]. Finally, it pre-processes the data, runs the predictions using the trained ML model, and does any necessary post-processing of the model output before returning the response to the caller. We call this the pre-process, predict, post-process patter