Definition
MLOps (Machine Learning Operations) is a discipline that applies DevOps and software engineering best practices to the deployment, monitoring, and lifecycle management of machine learning models in production. MLOps covers model versioning, automated retraining pipelines, performance monitoring, data drift detection, and infrastructure management to keep AI systems reliable and accurate over time.
How it works
Getting an AI model to work in a notebook is the easy part. Getting it to work reliably in production, at scale, for months - that is MLOps. Without it, models degrade silently. The data distribution shifts, the model's accuracy drops, and nobody notices until customers complain.
A mature MLOps practice includes model versioning (tracking which model is deployed and when), automated evaluation (testing new model versions against benchmarks before deployment), monitoring (tracking accuracy, latency, and cost in real time), and retraining pipelines (automatically updating models when performance drops below thresholds).
The level of MLOps investment should match the stakes. A content suggestion feature might need basic monitoring. A fraud detection system that blocks transactions needs rigorous evaluation pipelines, A/B testing infrastructure, and automated rollback capabilities. Over-engineering MLOps for low-stakes applications wastes budget. Under-investing for high-stakes ones creates risk.
How 1Raft uses MLOps
We build MLOps into every AI project from the start, scaled to the project's risk profile. For a fintech client's risk scoring model, we set up automated evaluation against a held-out test set, data drift detection that alerts when input distributions shift, and a retraining pipeline that triggers when accuracy drops below 92%. The model has been in production for 14 months without a manual intervention.
Related terms
AI/ML
Model Inference
Inference is the process of using a trained AI model to generate predictions or outputs from new inputs. When you send a prompt to an LLM and get a response, that is inference. It is where compute costs, latency, and user experience are determined.
AI/ML
Fine-Tuning
Fine-tuning is the process of training a pre-trained AI model on a smaller, domain-specific dataset to adapt its behavior for a particular task. It modifies the model's internal weights so it performs better on your specific use case without training from scratch.
AI/ML
Large Language Model (LLM)
A large language model is a neural network trained on massive text datasets to understand and generate human language. LLMs power chatbots, content generation, code assistants, and most modern AI products.
Related services
Next Step
Need help with MLOps?
We apply this in production across industries. Tell us what you are building and we will show you how it fits.