Data scientists use optimisation every day when training machine learning models, without even thinking about it. But there's another type of optimisation - that many data scientists are unaware of - that can be used to dramatically boost the business value of your ML outputs. This second layer transforms predictions into optimal decisions, and it's where the real impact often happens.

In this episode, Dr. Tim Varelmann joins Dr. Genevieve Hayes to explain how combining machine learning with decision optimisation creates solutions that go far beyond prediction, helping stakeholders make better decisions in uncertain environments.

You'll discover:

1. How decision optimisation differs from ML parameter tuning [02:19]
2. Why combining predictions with optimisation multiplies value [13:36]
3. The mindset shift needed to think in optimisation terms [22:59]
4. How to spot immediate optimisation opportunities in your work [23:42]

Guest Bio

Dr Tim Varelmann is the founder of Bluebird Optimization and holds a PhD in Mathematical Optimisation. He is also the creator of Effortless Modeling in Python with GAMSPy, the world’s first GAMSPy course.

Links

• Get Tim's 3 Step Guide to Add Optimisation to Your Data Science Skills
• Bluebird Optimization Website
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE