MLOps definition
MLOps (machine learning operations) is a set of practices and tools for deploying, monitoring and maintaining machine learning models reliably in production. It applies DevOps ideas such as version control, automated testing and CI/CD to data and models, so teams can retrain, release and roll back models safely and repeatably.
Why MLOps matters
Building a model in a notebook is the easy part. A widely cited 2015 Google paper, "Hidden Technical Debt in Machine Learning Systems", pointed out that model code is a small fraction of a real ML system, surrounded by data collection, feature pipelines, serving infrastructure and monitoring. MLOps is the discipline of building and running all of that so models reach production and stay useful.
Machine learning systems also fail differently from ordinary software. Code that passed its tests yesterday still works today, but a model can degrade silently because the data feeding it changed. MLOps adds data validation, model monitoring and retraining pipelines so those failures are caught by the team rather than by customers or auditors.
Core components of an MLOps pipeline
- Data versioning and validation: DVC, lakeFS, Great Expectations.
- Experiment tracking: MLflow, Weights & Biases.
- Feature store: Feast, Tecton or a cloud equivalent.
- Training pipelines: Kubeflow, Apache Airflow, SageMaker Pipelines, Vertex AI Pipelines.
- Model registry with versions, approvals and lineage.
- Serving: KServe, BentoML, NVIDIA Triton or managed endpoints.
- Monitoring: Evidently, Arize or cloud model monitors for drift, latency and accuracy.
MLOps vs DevOps
DevOps versions and tests code. MLOps must version and test three things: code, data and models. Its pipelines include data quality checks, training runs, evaluation gates that block a model performing worse than the current one, and continuous training that retrains on fresh data. Reproducibility also matters more: you need to know exactly which data, code and settings produced the model making a given decision.
MLOps maturity levels
Google Cloud's widely used framework describes three levels. At level 0, data scientists train models manually and hand them to engineers for deployment, often only a few times a year. Level 1 automates the training pipeline so models retrain on new data. Level 2 adds CI/CD for the pipeline itself, so changes to features or training code are tested and deployed automatically, like any other software release.
Large language model applications have produced LLMOps, a close relative. Instead of retraining weights, teams version prompts, retrieval indexes and model choices, run evaluation suites before every change, trace each request through tools and retrieval steps, and track token cost per feature. The core habits of versioning, testing and monitoring carry over unchanged.
How to get started with MLOps
Do not buy a platform first. Start with the model already in production or closest to it, and add the smallest set of practices that removes the biggest risk. Nexzem usually begins with experiment tracking, a model registry and basic monitoring, then automates retraining once the team has seen how quickly the data actually changes.
- Put training code, configs and data references under version control.
- Log every experiment and register the model that ships.
- Wrap serving in a container with health checks and rollback.
- Monitor inputs, predictions and outcomes, with alerts on drift.