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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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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?

Metrics 207
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3 Technical Innovations Ready to Drive Enterprise AI Adoption

IDC

Automated Machine Learning. Automated machine learning (AutomML) is the automation of the end-to-end process of applying machine learning (ML) to real-world problems. Here are just three of the technical innovations that enterprises can use to better leverage the disruptive power of AI: 1. Embedded AI.

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How AI Helps Banks Identify Cross-Selling and Upselling Opportunities

DataRobot

Many banks use DataRobot’s automated machine learning (AutoML) to interpret customer data. Banks are applying automation in their machine learning workflows for the following reasons: Machine learning allows banks to evaluate buyer behavior at the account level through an analysis of the most recent activities.

Banking 98
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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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Making Remarkable Energy Grids a Reality

CIO

These changes bring new challenges, but advancements in IT automation, artificial intelligence (AI) and machine learning (ML), and edge-computing capabilities will play a key role. Read our latest eBook and view our energy webpage to learn more about exciting advancements in energy.

Energy 167
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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.

Analytics 102