How choosing the wrong ML model cost a startup 6 months (and how to avoid it)

A client came to us after burning 6 months trying to build image recognition with basic regression models. The fix? Switching to CNNs solved it in 3 weeks.

This happens more than you'd think. So I wrote the model selection guide I give all our clients upfront.

The framework:

1. Start with your data - labeled, unlabeled, or feedback-based?
2. Define success - accuracy, speed, or interpretability?
3. Match the model - simple first, complex only when needed

Key insights:

• Linear regression isn't just for beginners (American Express uses it for fraud detection)
• Deep learning isn't always the answer
• Reinforcement learning shines for decision-making, not just games

Perfect for indie hackers who need to ship fast and iterate.

Full guide here → https://www.softwebsolutions.com/resources/machine-learning-models.html

Anyone else learned ML selection the hard way?