Remove Metrics Remove Predictive Modeling Remove Risk Management
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How Big Data Impacts The Finance And Banking Industries

Smart Data Collective

Here are a few of the advantages of Big Data in the banking and financial industry: Improvement in risk management operations. Big Data can efficiently enhance the ways firms utilize predictive models in the risk management discipline. Engaging the Workforce. Client Data Accessibility.

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Best BI Tools Examples for 2024: Business Intelligence Software

FineReport

From advanced analytics to predictive modeling, the evolving landscape of business intelligence is revolutionizing how data is processed and leveraged for actionable insights. Proactive Risk Management : BI tools enable organizations to proactively identify potential risks through predictive modeling and trend analysis.

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Why you should care about debugging machine learning models

O'Reilly on Data

That’s where model debugging comes in. Model debugging is an emergent discipline focused on finding and fixing problems in ML systems. In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Sensitivity analysis.

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Leveraging user-generated social media content with text-mining examples

IBM Big Data Hub

Text representation In this stage, you’ll assign the data numerical values so it can be processed by machine learning (ML) algorithms, which will create a predictive model from the training inputs. If necessary, make adjustments to the preprocessing, representation and/or modeling steps to improve the results.

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Generative AI copilots: What’s hype and where to drive results

CIO Business Intelligence

CIOs must also partner with CISOs, legal, human resources, and business leaders to build awareness of policies and develop a generative AI risk management strategy. As copilot technology capabilities are changing rapidly, leaders should frequently identify metrics and evaluate strategies. Generative AI, IT Strategy

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Predicting Movie Profitability and Risk at the Pre-production Phase

Insight

The process of selecting and engineering features is laborious but crucial since the success of any model depends heavily on the quantity and quality of the input data (recall: “garbage in, garbage out!”). Below is the result of a single XGBoost model trained on 80% of the data and tested on the unseen held-out 20%.

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What is a Data Pipeline?

Jet Global

Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. Job schedulers help coordinate the pipeline’s different stages and manage dependencies between tasks.