ML models that go into production need to handle large volumes of data, often in real-time. Unlike traditional technologies, AI and ML deal with probabilistic outcomes – in other words, what is the most likely or unlikely result.
Therefore, the moving parts of the ML model need close monitoring and swift action when deployed to ensure accuracy, performance, and user satisfaction.
Data scientists must grapple with three key influencing factors to ensure the proper development of ML models:
Data quality: Data can come from various sources in different volumes and formats. Since ML models are built on data, the quality, completeness, …
