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How to become an AI+ enterprise

IBM Big Data Hub

We refer to this transformation as becoming an AI+ enterprise. By becoming an AI+ enterprise, clients can realize the ROI not only for the AI use case but also for improving the related business and technical capabilities required to deliver AI use cases into production at scale. times higher ROI. times higher ROI.

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Only One Problem To Solve for Successful Data and Analytics

DataKitchen

Manufacturing production errors refer to mistakes or defects that occur during the manufacturing process. DataOps Observability refers to real-time monitoring, detecting, and diagnosing of data status, data pipelines, data tools, and other systems. Does it have ROI? BI tools like Tableau, PowerBI, etc.). Can I make this change?

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What you need to know about product management for AI

O'Reilly on Data

There may even be someone on your team who built a personalized video recommender before and can help scope and estimate the project requirements using that past experience as a point of reference. It’s difficult to be experimental when your business is built on long-term relationships with customers who often dictate what they want.

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Achieving cloud excellence and efficiency with cloud maturity models

IBM Big Data Hub

Cloud-native maturity models The first two maturity models refer more to an organization’s overall readiness; the cloud-native maturity model (CNMM) is used to evaluate an organization’s ability to create apps (whether built internally or through open source tooling) and workloads that are cloud-native.

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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

AGI, sometimes referred to as strong AI , is the science-fiction version of artificial intelligence (AI), where artificial machine intelligence achieves human-level learning, perception and cognitive flexibility. A key trend is the adoption of multiple models in production.

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Themes and Conferences per Pacoid, Episode 6

Domino Data Lab

Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. Given the two points above, that’s okay—there are good ways to direct data exploration toward ROI. Why does this matter?

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Expectations vs. reality: A real-world check on generative AI

CIO Business Intelligence

Ready to roll It’s shorter to make a list of organizations that haven’t announced their gen AI investments, pilots, and plans, but relatively few are talking about the specifics of any productivity gains or ROI. Pilots can offer value beyond just experimentation, of course. But where am I going to make money as an organization?”