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MLOps

MLOps is an abbreviation for machine learning operations, which refers to a collection of practices aimed at simplifying workflow processes and automating machine learning and deep learning implementations. It enables the deployment and maintenance of models for manufacturing on a big scale reliably and effectively. MLOps is gradually developing into a stand-alone strategy for the machine learning lifecycle, encompassing all stages from data collection to administration and monitoring. It will become a norm as artificial intelligence transitions from a creative activity to a component of daily business.

The K-Nearest Neighbours (KNN) algorithm belongs to the group of algorithms for supervised machine learning. Although it may be used to predict numeric data (regression), it is mostly used to predict non-numeric classes (classification). As a result, we have models for KNN regressor prediction as well as KNN classifiers. But the KNN classification method is quite popular in the sector. Since it just memorises the training data and does not generate a discriminative function from the data, it is most commonly referred to as the lazy learner algorithm. Since there is no training phase, it does not concentrate on developing the model but rather constantly looking at the closest data points to classify the data. The KNN model is frequently referred to as a non-parametric algorithm since it makes no assumptions about the data.

Machine Learning (ML) systems are multiplex, and a system has more failure possibilities the more multiplex it is. Building reliable ML algorithms requires having a clear understanding of what may go wrong. Together, let's examine concrete instances to illustrate potential hazards that could appear at various stages.

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