A Gooder AI case study: profiting on machine learning
The biggest hurdle for data science teams isn't building the model; it's proving its dollar value. This presentation shows how a dental group could translate a no-show prediction model into a clear business case worth $$$
It's about shifting the conversation from abstract metrics to tangible ROI.
Henry Castellanos is a data scientist extraordinaire. He goes beyond establishing a strong technical performance for his ML models to also maximizing their business value. Let this sink in: Most data scientists don't do that – most ML projects don't plan and sell predictive AI deployment according to the the explicit business value.
Interestingly, Henry points out that using Gooder AI (www.gooder.ai) to do this even bucks up his own confidence in his models and their business value.
Listen to Henry's presentation to see exactly how to bridge the gap from ML to real-world value.
To view this presentation as a video, go to: https://youtu.be/BT-GnnuN3jA
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Predictive AI Usually Fails Because It’s Not Usually Valuated (article)
In this episode, listen to a narration of Eric Siegel's article in Forbes:
Predictive AI Usually Fails Because It’s Not Usually Valuated
Most predictive AI deployments are scrubbed. Why? They didn't forecast the potential value in business terms like profit or savings.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/11/18/predictive-ai-usually-fails-because-its-not-usually-valuated/
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Predictive AI Only Works If Stakeholders Tune This Dial (article)
In this episode, listen to a narration of Eric Siegel's article in Forbes:
Predictive AI Only Works If Stakeholders Tune This Dial
Machine learning models can drive business operations to great benefit. But, to get there, stakeholders must determine how model probabilities trigger actions.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/11/25/predictive-ai-only-works-if-stakeholders-tune-this-dial/
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AI Drives Alphabet’s Moonshot To Save The World’s Electrical Grid (article)
In this episode, listen to a narration of Eric Siegel's article in Forbes:
AI Drives Alphabet’s Moonshot To Save The World’s Electrical Grid
AI is pivotal as global utilities tackle a looming crisis with the electrical grid. Here's how Alphabet uses AI to help the world keep the lights on.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/10/07/why-we-need-ai-alphabets-moonshot-to-save-the-worlds-electrical-grid/
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To Deploy Predictive AI, You Must Navigate These Tradeoffs (article)
In this episode, listen to a narration of Eric Siegel's article in Forbes:
To Deploy Predictive AI, You Must Navigate These Tradeoffs
Before deploying predictive AI, you must strike a balance between competing business factors. Here's how.
Access the original article here: https://www.forbes.com/sites/ericsiegel/2024/08/27/to-deploy-predictive-ai-you-must-navigate-these-tradeoffs/
Eric Siegel covers why machine learning is the most important, most potent, and most misunderstood technology. And did I mention most important?
Yup, it’s the most important – yet most new ML projects fail to deliver value. This podcast will help you:
- Make sure machine learning is effective and valuable
- Catch common machine learning oversights
- Understand ethical pitfalls – concretely
- Sniff out all the ”artificial intelligence” malarky
This podcast is for both data scientists and business leaders of all kinds – such as executives, directors, line of business managers, and consultants – who are involved in or affected by the deployment of machine learning.
To get machine learning to work, both the tech and business sides must make an effort to reach across wide chasm.
About the host:
Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI Applications Summit, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker. He wrote the bestselling ”Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die,” which has been used in courses at hundreds of universities, as well as ”The AI Playbook: Mastering the Rare Art of Machine Learning Deployment.” Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching the graduate *computer science* courses in ML and AI. Later, he served as a *business school* professor at UVA Darden. Eric has appeared on numerous media channels, including Bloomberg, National Geographic, and NPR, and has published in Newsweek, HBR, SciAm blog, WaPo, WSJ, and more.
https://www.machinelearningweek.com
http://www.bizML.com
http://www.machinelearning.courses
http://www.thepredictionbook.com
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