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Convergence, Epochs, And Inference In Machine Learning

This article explains convergence, epochs, and inference in Machine Learning.

Convergence

Imagine a machine learning model being trained to optimize database query performance. Initially, the model’s parameters are poorly set, causing the query to rely on a full table scan and resulting in slow execution times. As the model iteratively learns from performance feedback, it adjusts its parameters by introducing indexes, refining join strategies, and tuning the execution plan to reduce execution cost. With each iteration, the model’s performance improves and execution time decreases. Eventually, additional training and parameter adjustments produce little to no measurable improvement, indicating that the model has converged to an optimal or near-optimal configuration.

Epoch

An epoch is one complete pass through the entire training dataset.

Inference

Inference is using a trained model to make predictions on new, unseen data.

Posted By -	Karan Gupta

Posted On -	Monday, February 9, 2026

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