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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. One customer Sterblue offers a good example. investment to build intelligent machine learning labeling platform. The Aquarium team.

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The key to operational AI: Modern data architecture

CIO

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

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Why you should care about debugging machine learning models

O'Reilly Media - Data

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. Model debugging is an emergent discipline focused on finding and fixing problems in ML systems. We’ll review methods for debugging below. Not least is the broadening realization that ML models can fail.

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AI & the enterprise: protect your data, protect your enterprise value

CIO

For example, employees might inadvertently broadcast corporate secrets by inputting sensitive company information or source code into public-facing AI models and chatbots. Maintaining a clear audit trail is essential when data flows through multiple systems, is processed by various groups, and undergoes numerous transformations.

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How to know a business process is ripe for agentic AI

CIO

Does [it] have in place thecompliance review and monitoring structure to initially evaluate the risks of the specific agentic AI; monitor and correct where issues arise; measure success; remain up to date on applicable law and regulation? Feaver says.

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Unlocking the full potential of enterprise AI

CIO

Research from Gartner, for example, shows that approximately 30% of generative AI (GenAI) will not make it past the proof-of-concept phase by the end of 2025, due to factors including poor data quality, inadequate risk controls, and escalating costs. [1]

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Butter raises $7M to end ‘accidental’ customer churn due to payment failure

TechCrunch

It was there that he realized there was an astounding number of subscriptions that failed to renew or even go through to begin with due to payment-related issues. The accidental churn is often not just due to problems with renewals, where people get frustrated by failed attempts to charge their credit card, for example.