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From EDA to features: encoding, scaling, and the leakage traps in between
A machine learning model can achieve a perfect validation score and still be completely useless in production.
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Intro to scaling ML inference
Scaling machine learning inference efficiently is as critical as training a good model. As models grow larger and more complex, the challenge shifts from accuracy to throughput, latency, and cost optimization. This post introduces practical strategies, architectures, and tools used in 2025 to scale ML inference across CPUs, GPUs, and distributed environments.