Calculate the Value of Machine Learning Projects
Seu(s) apresentador(es)
Eric Siegel
CEO at Gooder AI, Founder at Machine Learning Week
Eric is CEO and co-founder of Gooder AI, a platform for maximizing the value of predictive AI projects. He is also the founder of the Machine Learning Week conference series, the author of the bestselling "Predictive Analytics" and "The AI Playbook", and the host of the Dr Data Show podcast. Previously, Eric was a professor at Columbia University and the UVA Darden School of Business.
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Summary
Calculating the business value of machine learning projects is essential for data scientists and business stakeholders to ensure effective deployment. The session, led by Eric Siegel, CEO of Gooder AI, highlights the importance of predictive AI and the need to estimate value before initiating machine learning projects. Predictive AI is key for improving operations across various sectors, yet many models fail to deploy due to a lack of business value estimation. The session introduces Gooder AI, a platform designed to link technical metrics with business KPIs, enabling data scientists to plan, sell, and approve AI deployments effectively. By focusing on business metrics like profit and savings, the session emphasizes aligning machine learning projects with organizational goals to maximize impact and drive successful deployments.
Key Takeaways:
- Predictive AI is essential for enhancing large-scale operations across industries.
- Most machine learning models fail to deploy due to a lack of business value estimation.
- Gooder AI helps connect technical metrics with business KPIs.
- Estimating business value before deployment is crucial for project success.
- Data scientists need to adopt a business-oriented approach to maximize impact.
Deep Insights
The Importance of Predictive AI
Predictive AI is a transformative technology that enhances operations across various sectors, from sales and fraud prevention to healthcare and manufacturing. Eric Siegel points out that predictive AI is distinct from generative AI, focusing on improving existing operations rather than creating new content. "Predictive AI is the technology you turn to improve your existing large-scale operations," Siegel notes. Despite its potential, many predictive AI projects fail to deploy due to a lack of business value estimation. Siegel argues that companies should invest equally in predictive and generative AI, as they address different needs and can complement each other.
Challenges in AI Deployment
A significant challenge in deploying AI models is the disconnect between technical metrics and business value. Many data scientists focus on technical metrics like precision and recall, which are often meaningless to business stakeholders. Siegel highlights that only 22% of data scientists report their initiatives usually deploy, primarily due to this disconnect. The session stresses the need for data scientists to adopt business metrics, such as profit and savings, to evaluate model performance. This shift enables better communication with stakeholders and increases the likelihood of project deployment.
Gooder AI: Connecting the Dots
Gooder AI is introduced as a solution to connect technical metrics with business KPIs. The platform allows users to estimate the business value of AI projects before deployment, addressing a critical gap in the industry. By focusing on business metrics, Gooder AI empowers data scientists to plan and sell AI deployments effectively. Siegel explains, "You can't say this model is worth a million bucks without considering the business context." The platform provides an interactive UI that helps users visualize potential deployment options and their business impact, making it easier to communicate with stakeholders.
Estimating Business Value
Estimating the business value of AI projects is vital for successful deployment. Siegel emphasizes that this estimation should be done before deployment to guide project decisions and ensure alignment with business goals. The session introduces the concept of using profit curves and savings curves to visualize the business impact of AI models. By moving from technical evaluation to business valuation, data scientists can better plan and communicate the potential value of their projects. This approach not only improves deployment rates but also enhances the overall impact of AI initiatives within organizations.