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AIOps vs. MLOps: Harnessing big data for “smarter” ITOPs

IBM Journey to AI blog

MLOps aims to bridge the gap between data science and operational teams so they can reliably and efficiently transition ML models from development to production environments, all while maintaining high model performance and accuracy. AIOps integrates these models into existing IT systems to enhance their functions and performance.

Big Data 106
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Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics

AWS Machine Learning Blog

Data preparation, feature engineering, and feature impact analysis are techniques that are essential to model building. These activities play a crucial role in extracting meaningful insights from raw data and improving model performance, leading to more robust and insightful results.

ML 98