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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

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MLOps and the evolution of data science

IBM Big Data Hub

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. How MLOps will be used within the organization.

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What is the impact of data science automation?

IBM Big Data Hub

On June 12th, IBM debuted AutoAI, a new set of capabilities for Watson Studio designed to automate critical yet time-consuming tasks associated with designing, optimizing and governing AI in the enterprise. As a result, data scientists can be liberated to commit more time to designing, testing and deploying machine learning models.

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AI Governance: Break open the black box

IBM Big Data Hub

Customers, employees and shareholders expect organizations to use AI responsibly, and government entities are demanding it. Failure to meet regulations can lead to government intervention in the form of regulatory audits or fines, damage to the organization’s reputation with shareholders and customers, and revenue loss.

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Data mesh: The key to innovation in government

Collibra

Watch the webinar It’s a digital world The truth is that in an increasingly digital world, the need for organizations to be data-driven has never been more pronounced. The federal government is no exception. But what does it mean for federal agencies to be more data-driven, and why is this shift important?

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SHARED INTEL: VCs pumped $21.8 billion into cybersecurity in 2021 — why there’s more to come

The Last Watchdog

Among them: an expanding digital footprint, growing attack surfaces, and increasing government regulation. Still, given the impact data science has had on other areas of software development, it seems likely that in the coming years one or more of these proposed solutions will yield a significant improvement in identity management systems.

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AI Governance: Why our tested framework is essential in an AI world

Collibra

As we speed into a new AI era, there’s a critical element that’s often missing when organizations rush forward in hyper-competitive markets to build scalable, trusted AI programs — and that’s AI governance. An AI governance framework offers a blueprint for how to create successful AI products.