DataOps
PulseAugur coverage of DataOps — every cluster mentioning DataOps across labs, papers, and developer communities, ranked by signal.
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MLOps Certified Professional Journey: Skills and Tools Detailed
This article outlines the path to becoming an MLOps Certified Professional, detailing the necessary skills and tools. It covers areas such as cloud platforms (Google Cloud, AWS, Azure), containerization (Kubernetes, Doc…
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AI Project Success Hinges on Clear Data Ownership, Experts Say
A key question for the success of any AI project is identifying who is responsible for the data it uses, according to Jacqueline DeStefano-Tangorra, president and CTO of DataOps. Many AI initiatives fail not due to tech…
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ZenML 0.80.0 released to tackle ML pipeline reproducibility
ZenML, an open-source MLOps framework, has released version 0.80.0, aiming to address the significant challenge of reproducibility in machine learning pipelines. The framework connects over 20 different tools, including…
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DataOps essential for AI model reliability over architecture
The reliability of an AI model is directly tied to the quality of its data pipeline, a concept known as DataOps. This discipline is crucial for ensuring AI systems remain accurate and trustworthy in production environme…
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AI hardware crunch intensifies with scarcity and rising costs
The AI industry is facing a significant hardware crunch, characterized by scarcity, extended lead times, and increased costs. This situation is shifting from an occasional issue to a persistent operational reality for A…
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Data governance fails without business unit input, risking AI project failure
Data governance initiatives often fail because they are solely managed by IT departments, neglecting crucial input from business units. This siloed approach leads to a lack of trust in data and poor adoption rates, as h…