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Deploy and Maintain More Models in Production With Dataiku 10

Dataiku

Dataiku 10 builds on core strengths related to model deployment and experiment tracking to bring additional tools to ML operators maintaining live models in production. Here, we'll highlight three key features that help make their job easier.

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Rising Tide Rents and Robber Baron Rents

O'Reilly on Data

As noted by economist Joseph Schumpeter , innovation—whether protected by patents, trade secrets, or just by moving faster and more capably than the competition—provides an opportunity to receive a disproportionate share of profits until the innovation is spread more widely. What Is Economic Rent?

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Avoid generative AI malaise to innovate and build business value

CIO Business Intelligence

Despite the promise generative AI holds for boosting corporate productivity, closing the gap between its potential and business value remains one of CIOs’ chief challenges. This could lead to more shadow AI , which could lead to more security threats and a wider attack surface. But how do you get there? This playbook can help.

Data Lake 129
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Bringing an AI Product to Market

O'Reilly on Data

The Core Responsibilities of the AI Product Manager. Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. Product managers for AI must satisfy these same responsibilities, tuned for the AI lifecycle.

Marketing 362
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Practical Skills for The AI Product Manager

O'Reilly on Data

In our previous article, What You Need to Know About Product Management for AI , we discussed the need for an AI Product Manager. In this article, we shift our focus to the AI Product Manager’s skill set, as it is applied to day to day work in the design, development, and maintenance of AI products. AI is no different.

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For the planet and people: IBM’s focus on AI ethics in sustainability

IBM Big Data Hub

“AI is an unbelievable opportunity to address some of the world’s most pressing challenges in health care, manufacturing, climate change and more,” said Christina Shim, IBM’s global head of Sustainability Software and an AI Ethics Board member. Therefore, it is critical to design and manage systems sustainably.

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DataRobot Automates and Simplifies AI/ML

David Menninger's Analyst Perspectives

Machine learning is valuable for organizations, but it can be hard to deploy. Our Machine Learning Dynamic Insights research identifies that not having enough skilled resources and difficulty building and maintaining ML systems are pressing challenges organizations face in applying ML.