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Are enterprises ready to adopt AI at scale?

CIO

To overcome those challenges and successfully scale AI enterprise-wide, organizations must create a modern data architecture leveraging a mix of technologies, capabilities, and approaches including data lakehouses, data fabric, and data mesh. To learn more about how enterprises can prepare their environments for AI , click here.

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How today’s enterprise architect juggles strategy, tech and innovation

CIO

In todays rapidly evolving business landscape, the role of the enterprise architect has become more crucial than ever, beyond the usual bridge between business and IT. In a world where business, strategy and technology must be tightly interconnected, the enterprise architect must take on multiple personas to address a wide range of concerns.

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Tecton.ai nabs $35M Series B as it releases machine learning feature store

TechCrunch

Tecton.ai , the startup founded by three former Uber engineers who wanted to bring the machine learning feature store idea to the masses, announced a $35 million Series B today, just seven months after announcing their $20 million Series A. “We help organizations put machine learning into production.

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Have we reached the end of ‘too expensive’ for enterprise software?

CIO

Before LLMs and diffusion models, organizations had to invest a significant amount of time, effort, and resources into developing custom machine-learning models to solve difficult problems. In many cases, this eliminates the need for specialized teams, extensive data labeling, and complex machine-learning pipelines.

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Aquarium scores $2.6M seed to refine machine learning model data

TechCrunch

Aquarium , a startup from two former Cruise employees, wants to help companies refine their machine learning model data more easily and move the models into production faster. investment to build intelligent machine learning labeling platform. Today the company announced a $2.6 Aquarium aims to solve this issue.

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When is data too clean to be useful for enterprise AI?

CIO

But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects. But that’s exactly the kind of data you want to include when training an AI to give photography tips. So, before embarking on major data cleaning for enterprise AI, consider the downsides of making your data too clean.

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Leveraging AMPs for machine learning

CIO

Such a large-scale reliance on third-party AI solutions creates risk for modern enterprises. Data scientists and AI engineers have so many variables to consider across the machine learning (ML) lifecycle to prevent models from degrading over time. However, the road to AI victory can be bumpy.