A Crash Course on Model Interpretability – Part 1

Model interpretability has been a long requested topic by many of you. So today, I am starting a new crash course series about it.

The first part is available here: A Crash Course on Model Interpretability – Part 1.

Why care?

A YouTube tutorial on sklearn doesn’t make anyone a data scientist, does it?

Think about it…

Do companies really care if you know specific tool stacks?

Not really.

What they care about is your ability to solve real business problems with data.

They aren’t hiring you for your knowledge of Python libraries—they’re hiring you because they face complex challenges that require data-driven solutions.

What truly matters is your ability to connect the dots between data and business outcomes.

• Can you transform raw data into actionable insights?

• Can you explain why a model works, not just how to implement it?

• Can you communicate results to stakeholders who don’t speak the language of data?

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.

Interpretability is one of those skills that goes beyond technical knowledge—it’s what many businesses care about the most.

When you can interpret a model, you’re not just answering technical questions—you’re answering 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.

That’s the goal of this series: to help you develop the skills that businesses are prioritizing more than ever before.

Read the first part here: A Crash Course on Model Interpretability – Part 1.

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:

• A Crash Course on Graph Neural Networks – Part 1

• Quantization: Optimize ML Models to Run Them on Tiny Hardware

• Conformal Predictions: Build Confidence in Your ML Model's Predictions

• A Crash Course on Causality – Part 1

• A Practical Guide to Scaling ML Model Training

• 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

Being able to code is a skill that’s diluting day by day.

Thus, the ability to make decisions, guide strategy, and build solutions that solve real business problems and have a business impact will separate practitioners from experts.

Happy learning, and I’ll see you tomorrow.