The End-to-End ML Lifecycle

What is the ML Lifecycle?

Software engineering traditionally moves linearly from requirements to coding, testing, and deployment.

Machine learning engineering is fundamentally cyclical and iterative. A deployed model is not the end of a project; it is the beginning of a continuous production monitoring and retraining loop.

Problem Framing ──► Data Ingestion ──► Feature Engineering ──► Model Training
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Continuous Monitoring ◄── Live Deployment ◄── Evaluation & Testing

The 6 Stages of the ML Lifecycle

1. Problem Framing and Metrics

Translate business objectives into technical machine learning tasks. Define target labels, prediction triggers, latency SLAs, and evaluation metrics.

2. Data Ingestion and Validation

Extract data from databases or data lakes. Run automated schema checks, remove corrupted samples, and handle missing values.

3. Feature Engineering

Transform raw data into informative features. Store created features in a unified Feature Store to ensure consistency across training and serving.

4. Model Training and Offline Evaluation

Train candidate model architectures. Perform hyperparameter tuning and evaluate model performance using held out validation sets and business metrics.

5. Deployment and Serving

Export optimized model artifacts. Deploy through shadow deployments, canary releases, or A/B tests to verify performance under live production traffic.

6. Continuous Monitoring and Retraining

Track live operational health, inference latency, prediction accuracy, and data drift. Trigger automated retraining pipelines when data distributions change.

Say this out loud

The end to end machine learning lifecycle is an iterative continuous loop managed by MLOps principles. It moves from problem framing and data validation through feature engineering, model training, deployment, and live monitoring. Because real world data continuously changes over time, models require ongoing performance tracking and automated retraining.

Followups to expect

1. What is the role of MLOps in the lifecycle? MLOps automates the transitions between lifecycle stages, providing continuous integration, automated model testing, reproducible data pipelines, and deployment monitoring.
2. When should you trigger model retraining? Retraining can be triggered on a fixed schedule (such as weekly), on data volume thresholds, or automatically when drift detection monitors alert to statistical distribution shifts.