MLOps, Not Magic: Scale ML Beyond Big Tech

Saturday, June 1, 2019

Updated: Wednesday, July 20, 2022
The original post has been refreshed with newer references. Image Credit: https://unsplash.com/photos/black-flat-screen-tv-turned-on-displaying-man-in-black-suit-5wThjqG6HBU

Most non–Big Tech companies I talk to want what Netflix, Amazon, and a handful of others already have: machine learning (ML) quietly shaping customer experiences at scale. Recommendations that feel personal. Chatbots that actually deflect calls. Targeted offers that don’t feel random.

But here’s the real question: Can you get those benefits without Big Tech’s budget, talent density, or tooling?

You can, if you stop thinking in terms of one-off ML projects and start treating ML like a product discipline supported by MLOps.

Think of MLOps as a Product Operating System

MLOps is usually defined as standardizing and streamlining the ML lifecycle. I’d go one step further: it’s the operating system that turns individual models into durable product capabilities.

Unlike traditional software, a model can’t simply be “set and forget.” Data changes. User behavior shifts. Regulations evolve. That means predictions decay. A model that works today can quietly become harmful or useless six months from now.

MLOps isn’t just “deployment plus monitoring”; it’s everything required to operationalize ML:

Designing models with deployment in mind
Building and versioning data pipelines
Deploying models in a repeatable way
Monitoring both model health and business impact
Governing how models behave and when they’re changed

Underneath that are a few deceptively simple questions: Which metrics will we monitor? At what thresholds do we treat those metrics as “worrisome”? How will we decide if a new model version really outperforms the current one?

These are product questions as much as technical ones. They force you to define value, risk, and success criteria upfront instead of after a demo.

MLOps vs. DevOps: Same Spirit, Different Risks

People often call MLOps “DevOps for ML.” That’s broadly true—the inspiration is the same: break team silos, automate repetitive work, and make releases boring instead of heroic.

Like DevOps, MLOps cares about:

Reliable, automated deployment pipelines
Collaboration between development and operations teams
Shorter feedback loops between building and learning

But two differences really matter for product leaders.

First, data is central. In ML, changing the data can change behavior as much as changing the code. Data quality, drift, and feature definitions are all part of the product surface.

Second, responsible and ethical AI is a first-class concern. You’re not just worried about uptime; you’re worried about fairness, explainability, and compliance with regulations like GDPR. A “successful” model that discriminates or leaks sensitive information is a business risk, not a win.

Thereforce, MLOps brings DevOps’ mindset of breaking silos, and then extends it to include data teams, legal, compliance, and business stakeholders. That’s where the product thinking really comes in.

Why Scaling ML is Hard Outside Big Tech

If you’re a student, mid-career practitioner, exec, or founder, you’ve probably seen some version of this pattern:

A team trains a model in a Jupyter notebook, has a great internal demo, and then … everything slows down. Months later, nobody is quite sure who owns it or whether it’s safe to touch.

There are four recurring reasons:

Deploying to production is bespoke every time. Without standard paths to production, each model becomes a special snowflake. Engineering has to reverse-engineer the data scientist’s work. Ops doesn’t want to support something fragile. Product can’t confidently promise timelines to the business.
Monitoring is fuzzy or missing. Even when models get deployed, teams often track only technical metrics (accuracy, AUC) and ignore business outcomes. For a chatbot, that might mean you’re not watching containment rate or customer satisfaction. For recommendations, maybe you’re not monitoring incremental revenue, only clicks.
Governance is an afterthought. Manual changes in production, unclear sign-offs, and no audit trail are common. That’s risky when models touch pricing, credit decisions, or customer communication. Governance isn’t just about regulators; it’s about protecting your brand and customers.
The lifecycle is opaque. Project files, data, models, and dashboards move between environments without a clear architecture. Data scientists can’t see what’s actually deployed. Data engineers don’t know when something needs to be tested or rolled out. Business owners don’t know when to expect improvements.

MLOps, done well, is about eliminating these one-off patterns. It replaces heroics with workflows, and personal memory with shared systems.

Scenario: A Mid-Sized Retailer Chasing “Amazon-like” Recommendations

Let’s make this concrete.

Imagine a national retailer with a solid e-commerce presence. The CEO says, “We need Amazon-style recommendations.” A small data science team exists, but the e-commerce platform is legacy, and budgets are tight.

You have choices.

You could attempt a complex deep learning recommendation system right away. It might look impressive in a slide deck, but getting it into production (integrated with the website, inventory, and email system) will be slow and fragile.

Or you take a product-minded, MLOps-aware path.

You start by defining value clearly: “We want to increase average order value and repeat purchases by showing relevant add-on items on the product detail page.”

You pick a simpler first model (maybe collaborative filtering or “customers who viewed this also bought”) because it’s easier to retrain and debug. You design it so that it can be served behind a basic API, with a feature flag controlling where it appears.

From the beginning, you define:

A small set of business metrics: uplift in add-to-cart rate, average order value, and a guardrail against promoting out-of-stock items.
Model metrics: coverage (how often you can show a recommendation), and a simple relevance score based on click or conversion.
Operational rules: what happens if the model fails? Do you fall back to top sellers?

You map roles:

Data science owns the model
Engineering owns the API and integration
Product owns the success metrics
Merchandising plus legal review any constraints around data use

You implement minimum monitoring: a dashboard showing model performance alongside key business metrics, and alerts if coverage drops or error rates spike.

Is this “the ultimate” recommendation system? No. But it’s a testable product increment that fits your constraints and can be iterated on. And every piece (data pipeline, model, deployment path, monitoring) can be reused for future models.

That’s MLOps as product leverage: you’re building a system, not just a feature.

Governing Principles, Seen Through a Product Lens

The classic MLOps principles line up nicely with product responsibilities.

Compatible with deployment

From day one, you build models, data prep, and dashboards with the production environment in mind. As a product leader, that means saying “no” to experiments that can’t possibly be maintained or reproduced once they leave the notebook.

Safe and robust environment

You ensure pipelines are scalable and resilient for both batch scoring and real-time predictions. For product, reliability is part of the feature definition: what’s the user experience if the model is slow, wrong, or temporarily down?

Governed

You monitor data quality, model performance, and fairness over time, with clear accountability. Product participates in governance reviews, asking: “Is this still serving our users and stakeholders the way we intended?”

Able to be updated

You design for easy retraining and redeployment so models can adapt to new data and business needs. That becomes part of your roadmap: regular iteration cycles, champion/challenger tests, and planned improvements—not just emergency fixes.

How to Start When Resources are Constrained

If you don’t have a massive platform team, you can still make meaningful progress:

Pick one ML use case with clear value: a chatbot that deflects simple support tickets, a recommendation block, or a churn prediction model feeding into retention campaigns. Map the entire lifecycle: where the data comes from, how the model is trained, how it’s deployed, how you’ll monitor it, and who approves changes.

Then ask: which step is the most manual, error-prone, or “tribal knowledge”-based? Automate or standardize that first. Sometimes it’s retraining, sometimes deployment, sometimes even just logging the right metrics.

And make dependencies visible. Even a lightweight checklist or simple workflow diagram can surface gaps between data, engineering, product, and compliance.

For students and early-career folks, look for chances to touch this full lifecycle, not just the modeling. For mid-career practitioners, volunteer to own one use case end-to-end and introduce basic MLOps practices. For execs and founders, sponsor a flagship ML product and insist on lifecycle thinking, not just a flashy launch.

In the end, MLOps is how non–Big Tech organizations move from clever one-off models to a steady, compounding source of value. You eliminate one-off processes, reduce silos, raise awareness of dependencies, and build logical workflows that connect everyone involved.

That’s when ML stops being a slide in the strategy deck and starts behaving like a real product capability.