Remove Measurement Remove Risk Remove Risk Management Remove Visualization
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The journey to a mature asset management system

IBM Big Data Hub

Asset management and technological innovation Advancements in technology underpin the need for holistic grid asset management, making the assets in the grid smarter and equipping the workforce with smart tools. Robots and drones perform inspections by using AI-based visual recognition techniques.

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Automating Model Risk Compliance: Model Monitoring

DataRobot Blog

If the assumptions are being breached due to fundamental changes in the process being modeled, the deployed system is not likely to serve its intended purpose, thereby creating further model risk that the institution must manage. These observations would have spanned a distribution, which the model leveraged to make its forecasts.

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Automating Model Risk Compliance: Model Validation

DataRobot Blog

To start with, SR 11-7 lays out the criticality of model validation in an effective model risk management practice: Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses.

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Best BI Tools Examples for 2024: Business Intelligence Software

FineReport

By leveraging advanced analytics capabilities, businesses can uncover hidden opportunities and potential risks within their datasets, allowing them to proactively address challenges and capitalize on emerging trends. In addition to these advancements, another prominent trend in data analysis is the growing impact of data visualization.

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How PwC and SAP are doing right by helping clients unlock ESG value

CIO Business Intelligence

From 1 January 2024, the provisions relating to supplier risk management will also apply to companies with more than 1,000 employees. Through an extensive risk analysis , the business partner portfolio is gradually stratified and high-risk business partners are identified for full SCDDA compliance.

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Why you should care about debugging machine learning models

O'Reilly on Data

1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. That’s where model debugging comes in. Sensitivity analysis.

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Cropin’s agriculture industry cloud to provide apps, data frameworks

CIO Business Intelligence

Cropin Apps, as the name suggests, comprises applications that support global farming operations management, food safety measures, supply chain and “farm to fork” visibility, predictability and risk management, farmer enablement and engagement, advance seed R&D, production management, and multigenerational seed traceability.

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