Remove Machine Learning Remove Measurement Remove Uncertainty
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Measuring Models' Uncertainty: Conformal Prediction

Dataiku

For designing machine learning (ML) models as well as for monitoring them in production, uncertainty estimation on predictions is a critical asset. It helps identify suspicious samples during model training in addition to detecting out-of-distribution samples at inference time.

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What you need to know about product management for AI

O'Reilly on Data

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). Most of these rules focus on the data, since data is ultimately the fuel, the input, the objective evidence, and the source of informative signals that are fed into all data science, analytics, machine learning, and AI models.

Strategy 290
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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

People have been building data products and machine learning products for the past couple of decades. How will you measure success? So now we have a user persona, several scenarios, and a way to measure success. Business value : Align outputs with business metrics and optimize workflows to achieve measurable ROI.

Testing 174
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Real-time Data, Machine Learning, and Results: The Evidence Mounts

CIO Business Intelligence

From delightful consumer experiences to attacking fuel costs and carbon emissions in the global supply chain, real-time data and machine learning (ML) work together to power apps that change industries. more machine learning use casesacross the company. By Bryan Kirschner, Vice President, Strategy at DataStax.

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How to Set AI Goals

O'Reilly on Data

Technical sophistication: Sophistication measures a team’s ability to use advanced tools and techniques (e.g., PyTorch, TensorFlow, reinforcement learning, self-supervised learning). Technical competence: Competence measures a team’s ability to successfully deliver on initiatives and projects. Conclusion.

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AI Product Management After Deployment

O'Reilly on Data

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”. While useful, these constructs are not beyond criticism.