Data science and data engineering teams spend a significant portion of their time in the data preparation phase of a machine learning (ML) lifecycle performing data selection, cleaning, and transformation steps. It’s a necessary and important step of any ML workflow in order to generate meaningful insights and predictions, because bad or low-quality data greatly reduces the relevance of the insights derived.
Data engineering teams are traditionally responsible for the ingestion, consolidation, and transformation of raw data for downstream consumption. Data scientists often need to do additional processing on data for domain-specific ML use cases such as natural language and time series. For example, certain ML algorithms may be sensitive to missing values, sparse features, or outliers and require special consideration. Even in cases where the dataset is in a good shape, data scientists may want to transform the feature distributions or create new features in order to maximize the

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