Launch Machine Learning Projects That Work
Generative AI vs. Predictive AI
Generative AI is impressive but overhyped, as experts like Eric Siegel argue that its tendency to hallucinate makes predictive AI a more reliable choice for automating large-scale operations.
Power Your Business With Predictive Models
Machine learning, a branch of artificial intelligence, transforms raw data into predictive models that drive business decisions, exemplified by UPS's use of predictive analytics to optimize delivery routes, saving $350 million annually and reducing emissions.
Why ML Projects Fail (and What You Can Do About It)
Predictive analytics expert Eric Siegel emphasizes that successful machine learning projects require alignment between business stakeholders and data scientists, urging both sides to bridge their knowledge gaps to enhance project deployment and operational improvements.
Six Steps for Successful Deployment
Predictive analytics expert Eric Siegel emphasizes that successful machine learning projects require a strong foundation in business goals and collaboration between data scientists and stakeholders, advocating for his bizML framework to ensure effective deployment and continuous model improvement.
Create More Value for Your Business With Predictive Lift
The quote "There are lies, damn lies, and statistics" highlights how data manipulation can mislead, particularly in machine learning, where predictive expert Eric Siegel argues that "lift" is a more effective metric than accuracy for evaluating model performance.
Address Bias in Predictive AI
Predictive analytics expert Eric Siegel highlights that algorithms, often trained on flawed human data, can perpetuate biases, influencing critical decisions like resource access, and emphasizes the need for awareness and responsible practices to mitigate these social justice risks.
Look Past AI Hype
Generative AI tools like ChatGPT are currently in a boom phase, but predictive analytics expert Eric Siegel warns that mismanaged expectations could lead to another "AI winter," emphasizing the need for healthy skepticism and a focus on concrete value in AI projects.
Sell Your AI Project With a Value-Driven Pitch
Machine-learning consultant Eric King cautions against exaggerating AI's capabilities, while predictive AI expert Eric Siegel emphasizes that successful AI pitches should prioritize concrete business value, focus on deployment goals, and engage stakeholders through impactful demonstrations rather than just technology details.
With all the current hype surrounding large language models and generative AI, it’s easy to forget that artificial intelligence has been around for decades — and used to great effect in many business sectors since at least the mid-80s. But Eric Siegel has the receipts. Especially when it comes to a specific branch of AI called machine learning (aka “enterprise ML,” “predictive AI,” and “predictive analytics”). He also has the know-how to help businesses utilize this technology to supercharge their efficiency, profits, and customer experience. The key, he emphasizes, is to ensure that the focus on the business goal is as strong as the fascination with the technology.
Learning Objectives
- Differentiate between generative AI and predictive AI (aka “machine learning”).
- Generate use cases for machine learning that move beyond hype.
- Plan for successful machine learning project deployment.
- Analyze machine learning projects for potential bias.
- Build momentum for initiating machine learning projects by demonstrating their concrete business value.