Successful ML models require more than just training code — they need a pipeline that automates data flow, transformation, training, and evaluation.
Typical Pipeline Stages
- Data collection and preprocessing
- Feature selection and transformation
- Model training and validation
- Deployment and monitoring
Tools and Frameworks
Use tools like Scikit-learn Pipelines, MLflow, or Kubeflow to orchestrate and manage your workflow efficiently.
Why Pipelines Matter
They reduce human error, ensure reproducibility, and speed up experimentation — key for production-grade ML.
