Kubeflow

Kubeflow is used as a Kubernetes-native ML orchestration platform, providing a structured way to build, run, and operate end-to-end machine learning workflows in production environments.

It serves as the system layer for ML, bridging data pipelines, training workflows, and model deployment within a Kubernetes-based platform.

Key Capabilities

• Pipeline-Oriented ML Workflows
  Enables DAG-based pipelines for data preparation, training, validation, and deployment.

• Reusable & Versioned Components
  Encourages modular pipeline components that can be reused and evolved safely.

• Distributed Training Support
  Supports scalable training workloads through native Kubernetes operators.

• Notebook-Based Experimentation
  Provides managed notebook environments with controlled resource allocation.

• Model Deployment & Serving
  Enables controlled model rollout patterns, including scaling and staged deployments.

Experience & Platform Contribution

Designed and operated production-grade ML workflows using Kubeflow as part of a broader ML platform.

Key contributions included:

• Orchestrating end-to-end ML pipelines, from data ingestion to model deployment
• Structuring pipelines for reproducibility, validation, and controlled experimentation
• Integrating Kubeflow Pipelines with experiment tracking and model registries
• Supporting distributed training workloads within Kubernetes resource constraints
• Enabling consistent promotion of models across environments

Kubeflow acted as the backbone of the ML system, allowing teams to move from experimentation to production while maintaining governance, reproducibility, and operational clarity.