> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # What Is MLOps > Explains MLOps principles, lifecycle stages, and how Kubeflow on Kubernetes supports reproducible, automated, and production-ready machine learning workflows. All right — let's begin with a simple question: what exactly is MLOps? MLOps (machine learning operations) is the discipline that helps organizations move models from experimentation into reliable production systems. It blends machine learning, software engineering, and DevOps practices to create reproducible, scalable, and maintainable workflows. Rather than treating model training as a one-off task, MLOps applies engineering rigor to the entire machine learning lifecycle: data, models, infrastructure, and monitoring. MLOps enables data scientists, ML engineers, and platform teams to collaborate across the lifecycle of an ML application so models can be shipped, monitored, and iterated on in production. A slide diagram showing Machine Learning, Software Engineering, and DevOps feeding into a central MLOps (Machine Learning Operations) circle, with a caption: "Automate and manage the entire ML lifecycle." Why MLOps matters * Training a model is just one stage. After deployment, models face data drift, changing user behavior, and infrastructure constraints. * Without repeatable processes, simple training code quickly becomes brittle and unmaintainable: ```python theme={null} model.fit(X_train, y_train) ``` * MLOps provides CI/CD-style workflows, automated retraining, model versioning, and production monitoring so teams can operate ML systems reliably at scale. MLOps treats machine learning as a continuous feedback loop: define the business problem → collect and prepare data → train and evaluate models → deploy for inference → monitor and retrain when performance degrades. The machine learning lifecycle MLOps focuses on automating and governing each lifecycle stage so teams can reproduce experiments, validate models, and safely roll out updates. Common lifecycle stages include: * Business problem definition * Data collection and validation * Feature engineering and preprocessing * Model training and evaluation * Packaging and deployment * Serving (online or batch) * Monitoring, logging, and drift detection * Retraining and governance A circular infographic titled "The Machine Learning Lifecycle" showing stages like Business Problem, Data Collection, Data Preparation, Model Training, Evaluation, Deployment, Monitoring, and Retraining. The diagram emphasizes a continuous loop with icons for each stage. Where does Kubeflow fit? Kubeflow is a platform that helps teams implement MLOps practices on Kubernetes. It is not MLOps itself, but a set of components and integrations that make it easier to run reproducible ML workloads, automate pipelines, perform hyperparameter tuning, and serve models at scale. Below is a quick reference of core Kubeflow building blocks and their common uses: | Kubeflow Component | Purpose | Typical use case | | - | -: | - | | Notebooks | Interactive development environments | Experiment and iterate on models using Jupyter notebooks | | Pipelines | Orchestrate and automate ML workflows | Build reproducible end-to-end pipelines for training and deployment | | Katib | Hyperparameter optimization | Run automated experiments to tune model parameters | | KServe | Model serving and inference | Deploy scalable, production-ready model endpoints | | Profiles | Multi-tenant access control | Manage isolated namespaces and resources for teams | Each component maps to lifecycle stages shown earlier, enabling teams to move from prototyping to production with consistent tooling on Kubernetes. A presentation slide titled "Where Does Kubeflow Fit?" showing five Kubeflow components—Notebooks (experiment and develop), Pipelines (automate workflows), Katib (optimize models), KServe (serve and deploy), and Profiles (multi-tenant access)—running on Kubernetes. The image visually maps these MLOps tools across a single Kubernetes platform. Key takeaway Machine learning success is measured not only by model accuracy but by the organization's ability to operate models reliably in production. MLOps brings the processes, automation, and governance necessary to keep models production-ready. Kubeflow is one of the widely used platforms to implement these practices on Kubernetes. Production ML is more than serving predictions. Pay attention to monitoring, data quality, versioning, and automated rollback strategies to avoid silent model failures in production. A slide titled "Key Takeaway" showing a flow from "Experimentation" to "Production" and the caption "MLOps takes machine learning beyond experimentation, making it:". Below are four rounded boxes listing qualities: Reliable, Scalable, Automated, and Production-Ready. Links and references * Kubeflow documentation: [https://www.kubeflow.org/](https://www.kubeflow.org/) * Kubernetes documentation: [https://kubernetes.io/docs/](https://kubernetes.io/docs/) * Overview of MLOps practices: [https://developers.google.com/machine-learning/ops](https://developers.google.com/machine-learning/ops) (conceptual reference) This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.