**The Myth-Busting Reality of MLOps: Data Quality and Prepro

The Myth-Busting Reality of MLOps: Data Quality and Preprocessing are King

In the realm of MLOps (Machine Learning Operations), a common misconception prevails: data quality and preprocessing are merely secondary concerns. However, the harsh reality is that poor data quality can have devastating consequences on model performance, leading to biased outcomes, model drift, and even deployment failures.

Model Drift: The Silent Killer

Model drift occurs when a deployed model's performance decays over time due to shifts in the underlying data distribution. This can be caused by various factors, including changes in user behavior, new feature additions, or even data quality issues. When data quality is compromised, the model's accuracy and decision-making capabilities suffer, resulting in biased outcomes and decreased model performance.

The Bias Epidemic

Poor data quality can also perpetuate bias in machine learning models, leading to discriminatory outcomes and unfair dec...

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