A Crash Course on Model Interpretability – Part 2
A Crash Course on Model Interpretability – Part 2
A deep dive into interpretability methods, why they matter, along with their intuition, considerations, how to avoid being misled, and code.
Last week, I started a new crash course series on model interpretability.
The second part is available here: A Crash Course on Model Interpretability – Part 2.
Why care?
Model interpretability isn’t something we have always cared about, as shown in search trends:
For a long time, interpretability was a concern primarily limited to academia or niche industries like finance.
In academia, researchers would explain WHY their models perform better than other models in their research papers, present qualitative analysis, etc. (I did that too a couple of times in my research papers).
Most industry ML practitioners, however, were content with treating models as black boxes as long as they delivered accurate predictions.
But the demand for transparency is now more than ever before.
Why?
In my experience, the post-Transformer era marked a turning point when several organizational leaders became more serious about their business’ AI strategy.
While they were already solving business use cases with ML, since the applicability of ML grew across several downstream applications, the risks grew equally.
That’s the goal of this series: to help you develop the skills that businesses are prioritizing more than ever before.
When you can interpret a model, you’re not just answering technical questions but business questions.
Why is a customer likely to churn?
What factors are driving sales?
How could a strategy shift influence future growth?
But interpretability isn’t just about quantifying “trust” in a model.
It’s also an opportunity for continuous improvement.
Only when you unpack a model’s inner workings can you identify biases, improve performance, and optimize outcomes.
Read the first part here: A Crash Course on Model Interpretability – Part 1.
Read the second part here: A Crash Course on Model Interpretability – Part 2.
Have a good day!
Avi
P.S. We have discussed several other topics (with implementations) in the past that align with “business ML.”
Here are some of them:
Quantization: Optimize ML Models to Run Them on Tiny Hardware
Conformal Predictions: Build Confidence in Your ML Model’s Predictions
5 Must-Know Ways to Test ML Models in Production (Implementation Included)
Federated Learning: A Critical Step Towards Privacy-Preserving Machine Learning
Model Compression: A Critical Step Towards Efficient Machine Learning
At the end of the day, all businesses care about impact. That’s it!
Can you reduce costs?
Drive revenue?
Can you scale ML models?
Predict trends before they happen?
If you can’t do that, your knowledge of specific tools will only get you so far.
All these resources will help you cultivate those key skills.
Happy learning, and I’ll see you tomorrow.