A Crash Course on Model Interpretability – Part 3
A deep dive into interpretability methods.
A couple of weeks back, I started a new crash course series on model interpretability.
The third part is available here: A Crash Course on Model Interpretability – Part 3.
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 first part here: A Crash Course on Model Interpretability – Part 1.
Read second part here: A Crash Course on Model Interpretability – Part 2.
Read third part here: A Crash Course on Model Interpretability – Part 3.
Have a good day!
Avi
P.S> 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?
We have discussed several other topics (with implementations) in the past that align with such topics.
Here are some of them:
Learn sophisticated graph architectures and how to train them on graph data: A Crash Course on Graph Neural Networks – Part 1
Learn techniques to run large models on small devices: Quantization: Optimize ML Models to Run Them on Tiny Hardware
Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust: Conformal Predictions: Build Confidence in Your ML Model’s Predictions.
Learn how to identify causal relationships and answer business questions: A Crash Course on Causality – Part 1
Learn how to scale ML model training: A Practical Guide to Scaling ML Model Training.
Learn techniques to reliably roll out new models in production: 5 Must-Know Ways to Test ML Models in Production (Implementation Included)
Learn how to build privacy-first ML systems: Federated Learning: A Critical Step Towards Privacy-Preserving Machine Learning.
Learn how to compress ML models and reduce costs: Model Compression: A Critical Step Towards Efficient Machine Learning.
All these resources will help you cultivate key skills that businesses and companies care about the most.
SPONSOR US
Get your product in front of 100,000 data scientists and other tech professionals.
Our newsletter puts your products and services directly in front of an audience that matters — thousands of leaders, senior data scientists, machine learning engineers, data analysts, etc., who have influence over significant tech decisions and big purchases.
To ensure your product reaches this influential audience, reserve your space here or reply to this email to ensure your product reaches this influential audience.