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Why you should care about debugging machine learning models

O'Reilly on Data

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

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Generative AI readiness is shockingly low – these 5 tips will boost it

CIO Business Intelligence

As genAI caught fire in 2023, many organizations rushed to test and learn from the technology and harness it to grow productivity and improve processes. You’ll align desired near-term and future states to test-and-learn pilots as well as potential production projects. High-quality data will be the oil that makes your models hum.

IT 110
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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade. See Ribeiro et al.

Modeling 139
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10 master data management certifications that will pay off

CIO Business Intelligence

Developer, Professional Certification Mastering Data Management and Technology SAP Certified Application Associate – SAP Master Data Governance The Art of Service Master Data Management Certification The Art of Service Master Data Management Complete Certification Kit validates the candidate’s knowledge of specific methods, models, and tools in MDM.

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Creating value with generative AI in manufacturing

CIO Business Intelligence

Others are weighing the advantages of subscription-based business models where industrial equipment, automation, and processes are delivered as a service. Get in touch to arrange a workshop with Avanade to further explore use cases of Microsoft Copilot within the context of your business.

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AI governance is rapidly evolving — Here’s how government agencies must prepare

IBM Big Data Hub

In the context of AI, it can refer to the safety and ethics guardrails of AI tools and systems, policies concerning data access and model usage or the government-mandated regulation itself. Bolster development teams by inviting diverse, multidisciplinary teams to join them in these workshops as they assess ethics and model risk.

Risk 74
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Simplify Deployment and Monitoring of Foundation Models with DataRobot MLOps

DataRobot Blog

Large language models, also known as foundation models, have gained significant traction in the field of machine learning. These models are pre-trained on large datasets, which allows them to perform well on a variety of tasks without requiring as much training data. What Are Large Language Models?