MLOps ≠ DevOps for AI: Why Your Mental Model Needs an Upgrade
The most dangerous assumption in our field is thinking you can just "DevOps" your way through ML systems
Hey there,
It's Gourav here. After spending 10,000+ hours training DevOps teams across industries, you'd think I'd be the first to advocate that "DevOps principles solve everything." And don't get me wrong—I love DevOps. It's transformed how we build and ship software.
But this morning, I found myself sighing into my cup of chai after yet another call with a consulting client who confidently stated,
"Our DevOps team will handle the ML infrastructure because MLOps is just DevOps for AI, right?"
I've been in your shoes. As someone deeply embedded in both worlds, I need to be clear: that assumption is fundamentally flawed.
And I suspect you've encountered this oversimplification more times than you care to count.
Let me tell you why this mental model is not just incorrect but potentially harmful to your organization's ML initiatives. I've spent over 10,000 hours training teams across various organizations, and this misconception tops my list of "myths that cause ML projects to derail."
The Kitchen Analogy
Think of traditional software development as running a restaurant with a fixed menu. DevOps in this world is about making sure ingredients arrive on time, recipes are standardized, cooking is efficient, and dishes get to customers hot and fresh.
Now imagine a restaurant where the menu changes daily based on what customers ate yesterday, what's trending on Instagram, and even the weather forecast. Oh, and customers can send back dishes to be reformulated in real-time.
That's ML in production.
In this second restaurant, you're not just worried about the cooking and serving processes - you need systems to:
Track which ingredient combinations worked best
Monitor why customers are sending back certain dishes
Evaluate if today's popular dish will still taste good tomorrow
Know when a once-popular recipe starts underperforming
This is MLOps - a fundamentally different beast.
Where DevOps Ends and MLOps Begins
Here's what makes MLOps its own discipline:
1. The Data Dimension
In traditional software, your code is your product. In ML systems, your code is just the recipe - your data is both your ingredients AND your final product.
I was working with a healthcare AI team last month who spent 6 weeks optimizing their CI/CD pipeline only to realize they had no system for tracking how their training data evolved over time. When model performance suddenly dropped, they couldn't pinpoint which data changes caused it.
That's like a chef perfectly optimizing kitchen workflow but having no idea when the suppliers switched from organic to conventional produce.
2. The Experimentation Lifecycle
Traditional software follows a somewhat linear path from dev to production. ML systems require constant experimentation, with multiple models being developed simultaneously.
One of our financial services clients has over 200 model experiments running in parallel at any given time. Their DevOps infrastructure couldn't handle this. They needed systems to track:
Which feature combinations are being tested
What hyperparameters each experiment uses
How to compare performance across experiments
How to transition the winner to production without disruption
3. The Feedback Loop Mystery
In DevOps, you can usually trace issues back to specific code changes. In MLOps, your model might degrade without any code changes at all - just because the world (and data) changed around it.
I call this the "silent drift" problem. It's like your kitchen staff executing the exact same cooking process perfectly, but customers suddenly hating the food because their tastes changed over time.
4. The Governance and Reproducibility Challenge
One manufacturing client spent millions on an ML platform but couldn't tell regulators exactly how their current production model was trained or why it made specific predictions. Their DevOps team had built logging for code deployment but not for model lineage or decision explanation.
What This Means For You
If you're a MLOps practitioner, this isn't just semantic nitpicking. It affects how you:
Structure your team: You need ML engineers who are first-class citizens in your process, not just "consumers" of DevOps services. These are engineers who understand both data science and production environments
Build your infrastructure: Your platform needs experiment tracking, feature stores, and model monitoring - tools traditional DevOps teams rarely work with
Redefine operational boundaries: MLEs need deeper operational involvement than traditional developers typically do, blurring the lines between development and operations in new ways
Manage stakeholder expectations: "We deployed it to production" is just the beginning of your journey, not the end
The Hybrid Approach That Actually Works
The most successful ML teams I've worked with don't reject DevOps principles - they extend them and integrate Machine Learning Engineers deeply into the process. Here's what works:
Adopt CI/CD practices but adapt them for ML artifacts (models, datasets) not just code
Implement infrastructure-as-code but ensure it's flexible enough for experimentation
Automate testing but include data validation and model performance checks, not just unit tests
Embrace monitoring but expand it to include data drift, model drift, and prediction explanations
The Takeaway
MLOps isn't DevOps for AI. It's a superset that includes DevOps principles plus an entirely new layer of practices specific to the unique challenges of ML systems.
The next time someone says "our DevOps team can handle it," ask them how they plan to track feature attribution across model versions or detect concept drift in production. Their answer will tell you if they truly understand what MLOps entails.
What's been your experience? Have you encountered this misconception in your organization? Hit reply - I read every response, and your insights help shape future discussions.
Until next time,
Gourav
P.S. I'm launching the MLOps Minidegree Track with complete Roadmap to MLOps Mastery this month. If you're interested, subscribe to our Nerd Plan to get early access when the courses are released.