Cloud
MLOps
Building Production-Ready MLOps Pipelines with Kubeflow
Learn how to build end-to-end MLOps pipelines using Kubeflow for scalable machine learning model deployment and management.
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8 min read
Introduction
In today's fast-paced AI landscape, deploying machine learning models to production requires robust, scalable infrastructure. Kubeflow has emerged as the leading platform for building MLOps pipelines on Kubernetes.
Key Components- Kubeflow Pipelines: Workflow orchestration
- Katib: Hyperparameter tuning
- KServe: Model serving
- Notebooks: Iterative experimentation
bash
pip install kubeflow
# Define pipeline steps hereDesign for reproducibility first; scale comes later.
MLOps Lifecycle
graph TD; Data[Data Ingestion] --> Train[Model Training]; Train --> Deploy[Deployment]; Deploy --> Monitor[Monitoring]; Monitor --> Retrain[Retraining];