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Make Your Models Matter: What It Takes to Maximize Business Value from Your Machine Learning Initiatives

Cloudera

We are excited by the endless possibilities of machine learning (ML). We recognise that experimentation is an important component of any enterprise machine learning practice. Once a model is deployed, ensuring peak operational performance becomes the challenge. .

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Trusted AI Cornerstones: Performance Evaluation

DataRobot

In this installment, I’ll cover four key elements of trusted AI that relate to the performance of a model: data quality, accuracy, robustness and stability, and speed. The performance of any machine learning model is tightly linked to the data it was trained on and validated against. Quality Input Means Quality Output.

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Improve Underwriting Using Data and Analytics

Cloudera

To me, this means that by applying more data, analytics, and machine learning to reduce manual efforts helps you work smarter. Simply stated, this approach enables data to be collected from any location and reside in any location for analytics to then be performed. Step two: expand machine learning and AI.

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Not Getting the Most From Your Model Ops? Why Businesses Struggle With Data Science and Machine Learning

TIBCO - Connected Intelligence

Companies have begun to recognize the value of integrating data science (DS) and machine learning (ML) across their organization to reap the benefits of the advanced analytics they can provide. What are the barriers keeping businesses from operationalizing data science and machine learning? Reading Time: 2 minutes.

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Build Trustworthy AI With MLOps

In our eBook, Building Trustworthy AI with MLOps, we look at how machine learning operations (MLOps) helps companies deliver machine learning applications in production at scale. We also look closely at other areas related to trust, including: AI performance, including accuracy, speed, and stability.

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Metrics Matter: The 4 Types of Code-Level Data OverOps Collects

OverOps

Transactions & Performance Metrics. These performance metrics include things like throughput, or the number of transactions that occur during a given period of time, and response time baselines. Are there any blocked threads related to this failure? Was this CPU spike caused by the application?

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How universities are using AI to power operational efficiency

Trigent

The role of technology in the education industry has witnessed some monumental trendsetters, right from 2019, which saw the advent of Big Data , Internet of Things (IoT), and Machine Learning. Students are classified based on their learning ability and content designed to suit each learning style.