ML Pipeline Architecture

Production ML systems require reliable, automated pipelines that handle data ingestion, preprocessing, training, evaluation, and deployment.

Pipeline Components

Stage Tools Considerations
Data Ingestion Apache Kafka, Airflow Schema validation, data quality
Feature Engineering Spark, Pandas Feature store, versioning
Model Training TensorFlow, PyTorch Experiment tracking, reproducibility
Model Evaluation MLflow, Weights & Biases Metrics comparison, validation
Deployment Docker, Kubernetes, Seldon A/B testing, canary deployments
Monitoring Prometheus, Grafana Data drift, concept drift

MLflow Example

import mlflow

with mlflow.start_run():
    mlflow.log_param("learning_rate", 0.01)
    mlflow.log_metric("accuracy", 0.95)
    mlflow.sklearn.log_model(model, "model")