What Machine Learning Does Well
Machine learning is useful when you have repeatable decisions or patterns in data and want a system to learn from examples.
Common ML jobs include:
<ul>
<li>classification</li>
<li>ranking</li>
<li>forecasting</li>
<li>anomaly detection</li>
<li>recommendation</li>
</ul>
If the product repeatedly asks, "What is likely to happen?" or "Which item is most relevant?" ML may be a fit.

Which problem is best suited to a machine learning approach?

Machine learning is most useful when patterns exist in data and the decision repeats often enough to matter.

The Main Types of ML
<ul>
<li>Supervised learning uses labeled examples to predict an outcome.</li>
<li>Unsupervised learning finds patterns or clusters without labels.</li>
<li>Reinforcement learning learns by interacting with an environment and receiving feedback.</li>
</ul>
PMs do not need to train these models, but they should know which product problems map to which learning style.

Which type of ML is commonly used for customer segmentation?

Supervised learning requires labeled examples.

Where PMs Go Wrong with ML
Teams often overestimate what ML can do when:
<ul>
<li>labels are weak or inconsistent</li>
<li>training data does not match live behavior</li>
<li>there is no feedback loop after launch</li>
<li>the output is technically accurate but not actionable in the product</li>
</ul>
A model is not valuable just because it is clever.

What is one of the strongest questions a PM should ask before green-lighting an ML feature?

A highly accurate model is valuable even if no workflow changes because of it.

Operational Reality Matters
ML systems can degrade over time because the world changes. PMs should plan for:
<ul>
<li>monitoring</li>
<li>retraining or recalibration</li>
<li>feedback collection</li>
<li>exception handling</li>
</ul>
Launching the model is the start of operating it, not the end.

A churn model becomes less accurate after a pricing change and a new segment launch. What is the most likely PM concern?

ML features often need ongoing monitoring because user behavior and market conditions change after launch.