Last Updated on April 29, 2026 by Editorial Team
Author(s): Utkarsh Mittal
Originally published on Towards AI.
This article discusses the complexities and trade-offs of machine learning model serving, detailing how decisions revolve around three sources: latency, throughput, and cost. It emphasizes the importance of understanding these factors when deploying models in production and features practical examples and strategies to maintain efficiency, including latency requirements and architectural choices. It provides insights into challenges such as cold starts and the significance of training-serving skew, supporting these topics with numerous examples, analogies, and recommendations for best practices in model deployment and monitoring.
Read the full blog for free on Medium.
Published via Towards AI
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