Here's the uncomfortable truth about enterprise AI: most of it never ships.
Research consistently shows that 80% or more of ML projects stall before reaching production. Not because the models don't work. They often do, in controlled environments. The failure happens in the messy space between "it works on my laptop" and "it works for our customers."
That space is what MLOps addresses. See From Demo to Deployment for more on the consistency crisis.
The Gap Nobody Talks About
Data science teams build models in notebooks. They iterate on algorithms, tune hyperparameters, run experiments. Eventually they get something that works: good accuracy on holdout data, reasonable performance metrics, a working demo.
Then reality intrudes.
How do you deploy this to production infrastructure? How do you handle errors? What happens when the data distribution shifts? How do you retrain? How do you know if it's working next week, next month, next year?
These questions don't have answers in the notebook. And they're the questions that determine whether your AI project delivers value or becomes another expensive experiment that never launched.
What MLOps Actually Is
MLOps is the discipline of deploying and maintaining ML systems in production. It borrows from DevOps but addresses ML-specific challenges:
Data versioning: Code has git. What tracks your training data, features, and model artifacts with the same rigor?
Experiment tracking: Which hyperparameters produced this model? What was the training data? Can you reproduce this result?
Feature engineering: How do you ensure features are computed consistently between training and serving? A mismatch here silently breaks everything.
Model validation: Unit tests don't catch model failures. How do you validate that a model is ready for production: not just accurate, but fair, robust, and safe?
Deployment automation: Can you deploy a new model version reliably? Can you roll back if something goes wrong? Can you run A/B tests?
Monitoring: How do you know if your model is still working? Data drift? Prediction drift? Performance degradation?
Feedback loops: How do production insights flow back to improve models? How do you close the loop between deployment and development?
Supervision: Monitoring tells you what happened. But in production, you also need to control what's allowed to happen: enforcing constraints, blocking harmful outputs, maintaining safety boundaries in real time.
None of this is optional. Skip any piece and your ML project will eventually break, or never launch at all.
Why Data Science Isn't Enough
The skills that make a great data scientist are different from the skills that make a production-ready ML system:
| Data Science | MLOps | |--------------|-------| | Model accuracy | System reliability | | Algorithm selection | Infrastructure management | | Feature engineering | Pipeline automation | | Experimentation | Production monitoring | | Notebook workflows | Reproducible pipelines |
Neither skill set is more important. But most organizations invest heavily in data science and underinvest in MLOps. The result: talented scientists building models that never see users.
The Maturity Ladder
MLOps maturity isn't binary. Teams progress through stages:
Level 0: Manual Everything
Models built in notebooks. Deployment is "the data scientist runs a script." No monitoring. Retraining happens when someone notices the model isn't working.
This is where most teams start. It doesn't scale.
Level 1: Automated Training
Training pipelines exist. Experiments are tracked. Model artifacts are stored in a registry. Deployment might still be manual, but at least training is reproducible.
Level 2: CI/CD for ML
Automated testing for models and data. Continuous integration that validates changes. Automated deployment with staging environments and rollback capability.
Level 3: Continuous Learning
Production monitoring triggers retraining automatically. Feedback loops close. The system improves itself based on real-world performance.
Most organizations are somewhere between Level 0 and Level 1. Getting to Level 2 is what separates demos from products.
The LLM Twist
Large language models change some of the MLOps equation:
Less training, more prompting: Most teams use pre-trained models. "Model development" means prompt engineering, not algorithm tuning.
Different failure modes: Hallucinations, prompt injection, safety violations. These require different monitoring than traditional ML metrics.
Vendor dependency: When your model is an API, you have less control but also different responsibilities.
But the core MLOps principles still apply. You still need:
- Versioning (of prompts, not just code)
- Testing (of outputs, not just accuracy)
- Monitoring (of quality, safety, and cost)
- Feedback loops (from production back to improvement)
Call it LLMOps if you want. It's the same discipline with different specifics.
What Actually Works
Teams that ship ML to production share common practices:
Start with infrastructure before models. Set up versioning, experiment tracking, and basic monitoring before building the first model. It's easier to build good habits than to retrofit them.
Own the full lifecycle. Don't throw models over the wall from data science to engineering. The team that builds should be responsible for production, at least initially.
Invest in monitoring early. You won't know your model is broken if you can't measure it. Build monitoring from day one, not after the first incident.
Automate ruthlessly. Every manual step is a place where things can go wrong. Every automated step is reproducible and scalable.
Treat models like code. Version them. Test them. Review changes. Deploy them through pipelines, not scripts.
The Real Competitive Advantage
Everyone can access the same foundation models. Everyone can hire data scientists. Everyone can run experiments.
What separates companies that deploy AI from companies that demo AI is MLOps maturity. The ability to reliably, safely, continuously deliver ML capabilities to production.
It's not glamorous work. There's no Kaggle competition for best CI/CD pipeline. But it's what makes AI real.
80% of ML projects fail before production. The 20% that succeed have MLOps figured out.
If you're investing in AI without investing in operations, you're buying models you'll never ship. Start with monitoring. Graduate to supervision. Build the infrastructure that turns experiments into products.
