Increasing model reliability
We first need to identify potential points of failure. The initial phase where data ingestion occurs is critical. Data, the lifeblood of any ML model, needs to be accurate, relevant, and unbiased. Anomalies or biases in this data can lead to skewed model outputs, directly undermining the model’s reliability. Regular audits of data sources, coupled with sophisticated preprocessing techniques, can help identify and rectify these anomalies before they impact the model.
Errors during the model training phase, such as overfitting or underfitting, present another layer of potential failure. Overfitting occurs when a model learns the training data too well, capturing noise as if it were a signal, which then fails to generalize to new data. Underfitting, on the other hand, happens when the model cannot capture the underlying trend of the data. Both scenarios can drastically reduce the model’s effectiveness in real-world applications. Implementing...
