How do we build a robust ML pipeline?

How It Works:

An ML pipeline ingests raw data, preprocesses and cleans it, trains models, validates performance, and automates deployment with monitoring for drift and retraining triggers.

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Key Benefits:

• Repeatability: Consistent results across runs.
• Efficiency: Automates tedious steps.
• Governance: Tracks lineage and compliance.

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Real-World Use Cases:

• Retail analytics: End-to-end sales forecasting pipeline.
• Healthcare ML: Automated model updates on new patient data.