Differences between AutoML and MLOps

Let’s look at some of the differences between AutoML and MLOps in this tutorial.

AutoML automates model-building tasks, while MLOps provides the processes and infrastructure to operate models reliably at scale. Understanding their differences helps teams pick the right tools and responsibilities for each phase.

What is AutoML?

AutoML (Automated Machine Learning) automates repetitive, technical steps in building ML models — like feature engineering, model selection, and hyperparameter tuning. It lets non-experts obtain reasonable models quickly and speeds up experienced practitioners by handling routine search and experimentation. AutoML focuses primarily on producing the best model(s) for a given dataset and objective with minimal manual intervention.

What is MLOps?

MLOps (Machine Learning Operations) is the set of practices, tools, and culture for deploying, monitoring, scaling, and maintaining ML models in production. It covers versioning, CI/CD for models, automated testing, reproducibility, observability, and governance to ensure models remain reliable and compliant over time. MLOps connects ML development with operational engineering so models deliver consistent business value.

AutoML vs. MLOps

	AutoML	MLOps
Primary goal	Automate model creation and optimization to produce accurate models quickly.	Ensure models are deployed, monitored, scalable, reproducible, and maintainable in production.
Scope	Narrow: AutoML focuses on data preprocessing, feature engineering, model search, and tuning.	Broad: MLOps includes CI/CD, deployment, monitoring, data/ model versioning, and governance.
Main users	Data scientists, analysts, and non-experts who need model-building assistance.	ML engineers, DevOps/SRE, platform teams, and cross-functional stakeholders.
Typical outputs	A trained model or ensemble and evaluation reports (best candidate models).	Production-grade model deployments, pipelines, monitoring dashboards, and alerts.
Automation emphasis	High automation of experimentation and hyperparameter tuning.	High automation of deployment, testing, scaling, and lifecycle management.
Lifecycle stage	Development and experimentation.	Operationalization and maintenance (post-development).
Key technologies / tools	AutoML platforms and libraries (search/optimization engines, feature tools).	CI/CD systems, orchestration (Kubernetes), model registries, monitoring, data pipelines.
Skillset required	Basic ML knowledge; less heavy coding needed for many tasks.	Software engineering, DevOps, reliability engineering, security, and data engineering skills.
When to use	When rapid prototyping or automated model selection is needed, or teams lack deep ML expertise.	When models must run reliably in production with SLAs, audits, or continuous retraining.
Limitations	May hide model internals, produce less interpretable models, and be less tailored to niche problems.	Requires investment in infrastructure, process, and cross-team coordination; can be complex to set up.

Putting them together

AutoML and MLOps are complementary. Teams often use AutoML during experimentation to find strong model candidates, then apply MLOps practices to deploy, monitor, and maintain the chosen model in production. Choosing the right balance depends on your goals: speed and accessibility (lean on AutoML) versus reliability and governance (invest in MLOps).