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May 15, 2026
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Building a Scalable ML Pipeline with Python

How I designed an end-to-end machine learning pipeline that handles 10k predictions per second using FastAPI, Redis, and Celery.

Designing high-throughput machine learning pipelines requires careful orchestration of compute-intensive tasks. In this article, I discuss the architecture of a system designed to process over 10,000 predictions per second using Python.

Architecture Overview#

To achieve low-latency predictions, we decoupled the ingestion API from the model inference workers using a high-performance message broker. FastAPI acts as the lightweight web entry point, queueing tasks into Redis.

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Decoupling API ingestion from CPU-bound ML inference is critical to maintaining horizontal scalability.

Worker Autoscaling and Performance#

We deployed Celery workers running on optimized GPU nodes to run batch inferences. Using Redis as the message queue ensures that tasks are distributed evenly, avoiding bottlenecks and maintaining predictable latency profiles.