A machine learning model is just code. To make it a product, you need a pipeline that automates its birth, growth, and survival in production.
1The ML Pipeline Stages
Unlike a standard data pipeline that ends with a table, an ML Pipeline ends with a Model Artifact. The orchestration layer must handle Data Validation (checking for missing values), Feature Transformation (scaling, encoding), Hyperparameter Tuning, and finally Deployment. Using Airflow to manage these stages ensures that every version of a model can be traced back to the exact dataset and code used to create it.
ML_Pipeline_DAG:
Step_1: [Spark_Feature_Calc]
Step_2: [XGBoost_Train]
Step_3: [Model_Validation] (If ACC < 0.9 then FAIL)
Step_4: [Deploy_to_SageMaker]
Status: MLOPS_PIPELINE_ACTIVE2Solving Training-Serving Skew
One of the biggest killers of AI products is Training-Serving Skew—where the model sees data differently in the lab than in production. A Feature Store acts as the 'Single Source of Truth'. It provides a consistent interface for the Data Engineer to write features once and the Data Scientist to read them for both training (Batch) and inference (Streaming).
Feature_Store: [ONLINE_STORE, OFFLINE_STORE]
Action: GET_FEATURES(entity_id='user_123')
Source: UNIFIED_FEATURE_REGISTRY
Status: TRAINING_SERVING_SKEW_ELIMINATED