Data Preparation, DataOps and Events

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

IBM Journey to AI blog

AUGUST 12, 2024

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

Big Data Big Data ML ML

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

AWS Machine Learning Blog

JULY 31, 2023

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 ML Data Preparation Machine Learning

Data Science Current

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

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

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