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This cluster provides a comprehensive view of the concepts, methods, and technologies of Artificial Intelligence and Machine Learning. It covers fundamental principles, use cases, and current trends in the industry.
This segment addresses the role of data in the machine learning context. It includes data types, data quality, feature definition, preprocessing, and transformation. The focus is on structurally preparing data as the basis for model training, not on analytical evaluation or reporting.
Preparation and standardization of raw data through cleaning, transformation, and normalization to improve analyses and models.
Concepts and practices for transforming raw data into informative features to improve predictive models.
Method for centrally storing, versioning and serving ML features for training and inference.