Why 99% of

Machine Learning Models

Die Before Deployment?

3rd Mar 2024
Let's be Brutal.

Machine learning is not magic. It doesn’t work just because you trained a model and got a 95% accuracy score. And yet, we see this over and over again—brilliant ML models getting stuck in limbo, never making it to deployment.

The truth? 99% of machine learning models never make it to production. Not because they aren’t useful, but because businesses, researchers, and even experienced engineers fundamentally misunderstand what it takes to make ML operational.

So, let’s talk about it. Why do so many ML models die before deployment? More importantly, how do you actually get an ML model to work in the real world?

1. Data Isn’t the New Oil—It’s a Pile of Trash (Unless Cleaned Properly)

Let’s be real. Most datasets are a mess. You have missing values, duplicates, inconsistent labels, and worst of all—data that doesn’t represent real-world scenarios.

A model trained on clean, structured data in a lab setting will collapse the moment it faces the chaotic, unpredictable reality of production data. It’s like training a soldier in a video game and then dropping them into actual combat.

• Solution: Data-centric AI is more important than model-centric AI. If your model isn’t working, your problem is probably in the data.

2. Machine Learning Engineers Forget That Infrastructure Exists

ML engineers love playing with architectures, hyperparameters, and optimization tricks. But you know what they hate? Dealing with infrastructure.

ML models don’t live in Jupyter Notebooks. They need to be integrated into actual pipelines, monitored, retrained, and optimized for latency.

• Solution: If you don’t understand ML Ops, CI/CD, and deployment pipelines, your model will never leave your laptop. Period.

3. Accuracy Is a Scam—The Real World Doesn’t Care About Your Metrics

That 97% accuracy you’re so proud of? It might be useless.

• What’s the precision and recall?
• How does the model perform under real-world noise?
• How much does it drift over time?
• What happens when you scale it from 1,000 predictions to 10 million?

High accuracy in controlled settings means nothing if your model can’t handle distribution shifts, edge cases, and real-world variability.

• Solution: Optimize for robustness, interpretability, and adaptability—not just accuracy.

4. Models Don’t Run Themselves—They Need Constant Maintenance

An ML model is like a pet—it needs to be fed (data), checked for health issues (drift detection), and sometimes, it needs retraining (fine-tuning with fresh data).

Most businesses don’t have the resources or systems to maintain models post-deployment. They launch them and then forget they exist until performance tanks.

• Solution: If you don’t have a plan for post-deployment monitoring and retraining, don’t even bother deploying the model.

5. Businesses Want ROI, Not Fancy Math

At the end of the day, ML models need to make or save money.

• If your fraud detection model reduces financial losses, it’s valuable.
• If your NLP chatbot speeds up customer service, it’s valuable.
• If your cutting-edge GAN produces “cool” images but doesn’t generate revenue, it’s just a fun experiment.

A CEO won’t care that you used a state-of-the-art transformer with 1.3 billion parameters if it doesn’t impact business KPIs.

• Solution: When it comes to Business Intelligence, ML is not just research. ML is a tool. Think like an entrepreneur, not just an engineer.