When & When NOT to use Machine Learning:
As its adoption in the industry quickly grows, ML has proven to be a powerful tool for a wide range of problems. Despite an incredible amount of excitement and hype generated by people both inside and outside the field, ML is not a magic tool that can solve all problems. Even for problems that ML can solve, ML might not be an optimal solution.
So, before starting an ML project, we must ask whether ML is necessary and cost-effective
Some applications where use of ML have been widely adopted are:
- Predictive Typing
- Machine Translation
- Fraud Detection
- Price Optimization
- Churn Prediction
- Automated Support Ticket Classification
- Brand Monitoring
- Sentiment Analysis
- Healthcare applications
When to use ML
ML is an approach to learn complex patterns from existing data and use these patterns to make predictions on unseen data.
| Condition | Explanation |
|---|---|
| The system has patterns to learn | ML solutions only work when there are patterns to learn |
| Data is available or can be collected | Without data, there are NO patterns to learn |
| Patterns are complex | If it can be solved by simple heuristics, you don’t need ML |
| The problem is at scale | ML shines when operating at scale |
| Predictions must change (non-static) | If the answer never changes, just hardcode it |
When NOT to use ML
- When the task is simple enough for hand-crafted rules
- When cost-effectiveness is not achieved
- When you need 100% explainability
- When the error cost is too high and you need deterministic outcomes