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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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Anomaly detection in machine learning: Finding outliers for optimization of business functions

IBM Big Data Hub

Anomalies are not inherently bad, but being aware of them, and having data to put them in context, is integral to understanding and protecting your business. The challenge for IT departments working in data science is making sense of expanding and ever-changing data points.

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Five machine learning types to know

IBM Big Data Hub

The validation and training datasets that undergird ML technology are often aggregated by human beings, and humans are susceptible to bias and prone to error. ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions.

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How to choose the best AI platform

IBM Big Data Hub

AI platforms offer a wide range of capabilities that can help organizations streamline operations, make data-driven decisions, deploy AI applications effectively and achieve competitive advantages. Visual modeling: Combine visual data science with open source libraries and notebook-based interfaces on a unified data and AI studio.

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Port Covington, MD re-emerges as ‘CyberTown, USA’ — ground zero for cybersecurity research

The Last Watchdog

When CyberTown, USA is fully built out, it’s backers envision it emerging as the world’s premier technology hub for cybersecurity and data science. It’s mission has been to seek out and assist government cyber specialists in a position to enter the private sector and build commercial cyber and data science companies.

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Foundational models at the edge

IBM Big Data Hub

Foundational models (FMs), which are trained on a broad set of unlabeled data at scale, are driving state-of-the-art artificial intelligence (AI) applications. First, enterprises produce a vast amount of unlabeled data, only a fraction of which is labeled for AI model training. What are large language models?

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Top 6 Kubernetes use cases

IBM Big Data Hub

But Docker lacked an automated “orchestration” tool, which made it time-consuming and complex for data science teams to scale applications. Overall, Kubernetes provides the flexibility, portability and scalability needed to train, test, schedule and deploy ML and generative AI models.

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