What is MLOps?
MLOps is the practice of combining machine learning, DevOps, and data engineering to reliably build, deploy, and maintain ML models in production.
It applies automation, version control, and continuous integration/delivery pipelines to the full ML lifecycle, including data preparation, model training, testing, deployment, and monitoring.
Key ideas include tracking experiments, managing model and data versions, detecting performance drift, and using infrastructure-as-code to scale serving reliably.
MLOps teams collaborate across roles so models move smoothly from research notebooks to robust, observable production systems.
Example
An e-commerce team uses MLOps pipelines to automatically retrain a product-recommendation model weekly on fresh user data, run validation tests, and deploy the updated model to their serving cluster with zero downtime.
Why it matters
Most ML projects fail to deliver value because models degrade or break in production; MLOps closes the gap between experimentation and reliable, scalable deployment.
Frequently asked questions
DevOps focuses on software code; MLOps adds handling of data, models, and experiments that change over time and require specialized testing and monitoring.
Related terms
An API (Application Programming Interface) is a standardized set of rules that lets software applications request services or data from each other. In AI infrastructure, it typically means exposing machine learning models as callable endpoints for inference or training.
CUDA is NVIDIA's platform and programming model that lets developers run general-purpose computations on NVIDIA GPUs instead of just CPUs.
Knowledge distillation is a technique that transfers knowledge from a large, complex 'teacher' model to a smaller 'student' model so the student can achieve similar performance with far less compute and memory.
Edge AI runs AI models directly on local devices such as phones, cameras, or sensors instead of sending data to remote cloud servers.
In AI/ML infrastructure, an endpoint is a deployed URL or network address that exposes a trained model so applications can send data and receive predictions via API calls.
FLOPs stands for floating-point operations and counts the total number of arithmetic calculations (additions, multiplications) a neural network performs during a forward or backward pass.