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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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How Data Observability Helps to Build Trusted Data

Precisely

Trusted data is crucial, and data observability makes it possible. Data observability is a key element of data operations (DataOps). The best data observability tools incorporate artificial intelligence (AI) to identify and prioritize potential issues.

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Secrets from Data Governance Leaders: DGIQ West 2023 (June 5 – 9)

Alation

If you’re not familiar with DGIQ, it’s the world’s most comprehensive event dedicated to, you guessed it, data governance and information quality. This year’s DGIQ West will host tutorials, workshops, seminars, general conference sessions, and case studies for global data leaders.

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What Is Data Observability and Why You Need It?

Precisely

Systems and data sources are more interconnected than ever before. A broken data pipeline might bring operational systems to a halt, or it could cause executive dashboards to fail, reporting inaccurate KPIs to top management. Data observability is a foundational element of data operations (DataOps).