Remove Cost-Benefit Remove Data Processing Remove Experimentation
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Experimentation and Testing: A Primer

Occam's Razor

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). Experimentation and testing help us figure out we are wrong, quickly and repeatedly and if you think about it that is a great thing for our customers, and for our employers. Counter claims?

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Your New Cloud for AI May Be Inside a Colo

CIO Business Intelligence

Enterprises moving their artificial intelligence projects into full scale development are discovering escalating costs based on initial infrastructure choices. Many companies whose AI model training infrastructure is not proximal to their data lake incur steeper costs as the data sets grow larger and AI models become more complex.

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GenAI sticker shock sends CIOs in search of solutions

CIO Business Intelligence

The early bills for generative AI experimentation are coming in, and many CIOs are finding them more hefty than they’d like — some with only themselves to blame. By understanding their options and leveraging GPU-as-a-service, CIOs can optimize genAI hardware costs and maintain processing power for innovation.”

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

After experimentation, the data science teams can share their assets and publish their models to an Amazon DataZone business catalog using the integration between Amazon SageMaker and Amazon DataZone. The applications are hosted in dedicated AWS accounts and require a BI dashboard and reporting services based on Tableau.

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Why enterprise CIOs need to plan for Microsoft gen AI

CIO Business Intelligence

The cost of OpenAI is the same whether you buy it directly or through Azure. Organizations typically start with the most capable model for their workload, then optimize for speed and cost. Platform familiarity has advantages for data connectivity, permissions management, and cost control. It’s a very different beast.”

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3 steps to eliminate shadow AI

CIO Business Intelligence

These same decision-makers identify a host of challenges in implementing generative AI, so chances are that a significant portion of use is “unsanctioned.” If the code isn’t appropriately tested and validated, the software in which it’s embedded may be unstable or error-prone, presenting long-term maintenance issues and costs.

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7 steps for turning shadow IT into a competitive edge

CIO Business Intelligence

Still, there is a steep divide between rogue and shadow IT, which came under discussion at a recent Coffee with Digital Trailblazers event I hosted. Communicate clearly and often about policies and their reasons and benefits, create a culture of feedback and collaboration, and be agile and willing to adapt policies as user needs evolve.”

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