What does an MLOps Engineer actually do?
The bridge between a model that works on a laptop and one that works in production
Training a good model is only part of the problem — the harder part is often getting it into production reliably, keeping it fed with fresh data, watching it for drift, and rolling out updates without breaking anything. That's the gap MLOps exists to close.
It sits at the intersection of data engineering, software engineering, and DevOps — building the pipelines that move data and models through training, versioning, deployment, and monitoring, usually on top of cloud infrastructure and containers.
You don't need to be a research scientist to do this work well. Solid programming and infrastructure skills, a working understanding of how models are trained and evaluated, and comfort with distributed systems matter more than deep ML theory day-to-day.
The MLOps Engineer Roadmap
Work through these in order, from fundamentals to a monitored production system
MLOps Fundamentals
What MLOps is, and how it differs from the DevOps and DataOps roles it borrows from.
Programming & Scripting
The languages and tools every step of an ML pipeline is glued together with.
Machine Learning Fundamentals
Enough ML to speak the same language as the data scientists you'll be supporting.
Data Engineering for ML
Get clean, versioned data flowing reliably into training and inference.
Experiment Tracking & Model Management
Know which model, trained on which data, with which parameters, produced which result.
Model Packaging & Serving
Get a trained model behind an endpoint that other systems can actually call.
Orchestration & Pipelines
Chain training, evaluation, and deployment steps into a pipeline that runs itself.
Infrastructure & Cloud
Where training and serving actually run, and how compute gets provisioned for it.
Monitoring & Observability
Know when a model in production is quietly getting worse, not just when it's down.
Model Governance & Security
Prove which model made which decision, and keep that decision auditable and fair.
Scaling & Advanced Topics
Where MLOps goes once a single model on a single machine isn't enough.
GitHub Projects
Real, buildable projects to put on your own GitHub
Made With ML
Work through a complete, production-minded ML project covering data pipelines, experiment tracking, serving, and CI/CD end to end.
MLflow Source
Study how experiment tracking, model registries, and packaging are actually implemented in one of the most widely used MLOps tools.
Build a Drift Monitoring Dashboard
Use an open-source ML monitoring library to build a dashboard that tracks data drift and model performance on a real dataset.
Deploy a Kubeflow Pipeline
Set up Kubeflow on a local or cloud Kubernetes cluster and deploy a full training-to-serving pipeline to practise orchestration hands-on.
Frequently Asked Questions
Common questions from people starting out with MLOps
Do I need to be a data scientist before becoming an MLOps engineer?
No. Many MLOps engineers come from a DevOps or backend engineering background and pick up enough ML fundamentals to work effectively with data scientists — a working understanding of how models are trained and evaluated matters more than the ability to design new architectures.
What's the difference between MLOps and DevOps?
DevOps focuses on shipping and operating software reliably. MLOps borrows that discipline but adds concerns specific to ML — data versioning, experiment tracking, model drift, and retraining — that don't exist in traditional software deployment.
Is Kubernetes required for MLOps?
Not strictly, especially for smaller teams using a managed cloud ML platform, but it's extremely common at scale for orchestrating training jobs and serving infrastructure, and most MLOps job postings expect at least working familiarity with it.
Which cloud ML platform should I learn first?
Whichever your target employer uses is the practical answer, but AWS SageMaker has the widest market share and documentation, making it a reasonable default to start with if you don't have a specific employer in mind.
What is model drift, and why does it matter?
Drift is when the statistical properties of incoming data — or the relationship between inputs and outputs — shift away from what a model was trained on, quietly degrading its accuracy. Monitoring for it is what lets a team catch a failing model before its predictions cause real damage.
What's LLMOps, and how does it relate to MLOps?
LLMOps is MLOps applied specifically to large language models — it shares the same core practices (versioning, monitoring, CI/CD) but adds concerns like prompt management, retrieval pipelines, and evaluating generative rather than purely predictive outputs.
Track complete
From data pipelines and experiment tracking to model serving, orchestration, and drift monitoring — that's the core of what's expected of a production MLOps engineer. Keep building, and put it to work on a real model.
Where next?
Keep exploring by domain or drill into a single skill