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Five Important Trends in Big Data Analytics

Flipboard

Over the last few years, with the rapid growth of data, pipeline, AI/ML, and analytics, DataOps has become a noteworthy piece of day-to-day business New-age technologies are almost entirely running the world today. Among these technologies, big data has gained significant traction. This concept is …

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

IBM Journey to AI blog

Instead, businesses tend to rely on advanced tools and strategies—namely artificial intelligence for IT operations (AIOps) and machine learning operations (MLOps)—to turn vast quantities of data into actionable insights that can improve IT decision-making and ultimately, the bottom line.

Big Data 106
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The Audience for Data Catalogs and Data Intelligence

Alation

The product concept back then went something like: In a world where enterprises have numerous sources of data, let’s make a thing that helps people find the best data asset to answer their question based on what other users were using. And to determine “best,” we’d ingest log files and leverage machine learning.

DataOps 52
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The Shift from Models to Compound AI Systems

BAIR

Machine learning models are inherently limited because they are trained on static datasets, so their “knowledge” is fixed. Therefore, developers need to combine models with other components, such as search and retrieval, to incorporate timely data. Operation: LLMOps and DataOps. Systems can be dynamic.

AI 145
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Data Trends for 2023

Precisely

Read Here are the top data trends our experts see for 2023 and beyond. DataOps Delivers Continuous Improvement and Value In IDC’s spotlight report, Improving Data Integrity and Trust through Transparency and Enrichment , Research Director Stewart Bond highlights the advent of DataOps as a distinct discipline.

DataOps 52
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What Do Data Scientists Do? A Guide to AI Maturity, Challenges, and Solutions

DataRobot Blog

The future of business depends on artificial intelligence and machine learning. According to IDC , 83% of CEOs want their organizations to be more data-driven. Data scientists could be your key to unlocking the potential of the Information Revolution—but what do data scientists do? What Do Data Scientists Do?

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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).