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Machine Learning
MLOps Architecture
Monolithic MLOps Pipeline
All stages of the ML lifecycle run in a single orchestrated pipeline managed by tools like Airflow or Prefect.
Intent & Description
📋 Context
The monolithic pipeline is the simplest architecture, suitable for small teams running a few models. Data ingestion, feature engineering, training, evaluation, and deployment are steps in one DAG.
Real-world Use Case
Small teams running 5-10 models where simplicity and low operational overhead are priorities.
Source
Advantages
- Simple to understand and debug
- Single codebase
- Easy to set up
- Low operational overhead
Disadvantages
- Does not scale beyond 5-10 models
- Teams step on each other
- Single point of failure
- Hard to reuse components
Implementation Example
# Monolithic Pipeline
with DAG("ml_pipeline") as dag:
ingest = ingest_data()
features = feature_engineering(ingest)
train = training(features)