Identifying, collecting, and transforming data is the foundation for machine learning (ML). According to a Forbes survey, there is widespread consensus among ML practitioners that data preparation accounts for approximately 80% of the time spent in developing a viable ML model.
In addition, many of our customers face several challenges during the model operationalization phase to accelerate the journey from model conceptualization to productionization. Quite often, models are built and deployed using poor-quality, under-representative data samples, which leads to more iterations and more manual effort in data inspection, which in turn makes the process more time consuming and cumbersome.
Because your models are only as good as your training data, expert data scientists and practitioners spend an enormous time understanding the data and generating valuable insights prior to building the models. If we view our ML models as an analogy to cooking a meal, the importance of high-quality data for

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