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A phase of the machine learning lifecycle during which data scientists apply an ML algorithm to historical data to generate the desired model accuracy. This is an iterative process in which they review model results and then adjust features, tweak model parameters such as weights, or change ML techniques or algorithms, as needed.
July 3, 2019 - DataOps helps data engineers and scientists get machine learning right. Improve results with modular development, flexible execution and rigorous testing.
May 22, 2018 - Designing, deploying, and managing effective data architectures is critical to the success of machine learning initiatives. Implementations require...
April 14, 2021 - The lifecycle of machine learning projects spans data and feature engineering, model development, and ML operations or MLOps.