This week covered the process of refining prepared data into a form that models can learn — feature engineering and data quality. It's the latter part of MLA-C01 Domain 1 and the stage that most significantly determines model performance. It's also the domain where ML's maxim "data over algorithm" shines most brightly.
Today we weave Data Wrangler·Feature Store·Clarify into a single flow and review them together. The three tools handle transformation·management·validation respectively, and when linked together, they form a pipeline "from raw data to a trustworthy training dataset".