90% of ML Project Success Hinges on Data Quality

Data is the backbone of machine learning (ML) projects, with its quality directly impacting the success rate. A study shows that 90% of the effectiveness of ML models depends on the data used for training. Poor or biased data can lead to incorrect predictions, overfitting, or even societal harm when models are applied in real-world scenarios. To address these issues, MLOps (Machine Learning Operations) has emerged, focusing on automating and streamlining ML workflows. A key practice within MLOps is dataset tracking throughout the project lifecycle, ensuring reproducibility and traceability. MLflow, an MLOps tool, offers functionalities to log datasets, track metadata, and manage data drift. For instance, using the California Housing Dataset, MLflow can log the exact data used in experiments, allowing for future reference and consistency in model evaluation. This practice is crucial for transparency, reproducibility, and accountability, especially in regulated industries like healthcare or finance.

Source: towardsdatascience.com