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Module 04 · Page 5 of 20 · 9 sections

Model Training Pipeline

The stages from data preparation to a registered, deployed model, and the monitoring loop that triggers retraining.

03Data Pipeline and Data Engineering05Model Serving and Inference Architecture15MLOps and LLMOps
4.1

What is a Model Training Pipeline?

  • A series of steps to prepare data, train, validate and register a model in a reproducible way.
  • It ensures data flows through well-defined stages from raw data to a trained model.
  • Helps automate training and improves efficiency, consistency and scalability.
4.2

Key Goals

  1. 1Train high quality and accurate models.
  2. 2Ensure reproducibility of experiments.
  3. 3Handle large scale data efficiently.
  4. 4Track experiments and model versions.
  5. 5Enable continuous training and deployment.
  6. 6Reduce manual effort and errors.
4.3

Typical Training Pipeline Architecture

Data PreparationCollect, clean, features
TrainingTrain model, tune hyperparameters
ValidationMetrics, avoid overfitting
Model RegistrySave, version, metadata
DeploymentServe via API, monitor
4.4

1. Data Preparation

  1. 1Collect data from databases, files, APIs, etc.
  2. 2Handle missing values and duplicates.
  3. 3Perform data cleaning and normalization.
  4. 4Do feature engineering (create useful features).
  5. 5Split data into train, validation and test sets.
Related03Data Pipeline and Data Engineering
4.5

2. Training

  1. 1Choose the right algorithm (e.g. LR, RF, DL).
  2. 2Train the model using training data.
  3. 3Tune hyperparameters (grid search, random search).
  4. 4Use techniques like cross-validation.
  5. 5Track training metrics (loss, accuracy, etc.).
4.6

3. Validation and Evaluation

  1. 1Evaluate the model on validation and test data.
  2. 2Use metrics like accuracy, precision, recall and F1-score.
  3. 3Perform error analysis to identify issues.
  4. 4Check for overfitting or underfitting.
  5. 5Compare with baseline models.
Related14Evaluation and Testing
4.7

4. Model Registry

  1. 1Store trained models with versioning.
  2. 2Save metadata (parameters, metrics, dataset info).
  3. 3Tools: MLflow, Model Registry, S3, etc.
  4. 4Helps in model governance and easy rollback.
4.8

5. Deployment

  1. 1Deploy the model as a REST API or batch job.
  2. 2Use containers (Docker) and orchestration (Kubernetes).
  3. 3Handle scaling and load balancing.
  4. 4Ensure low latency and high availability.
  5. 5Integrate with the application for real-time or batch inference.
Related05Model Serving and Inference Architecture
4.9

6. Monitoring and Feedback

  1. 1Monitor model performance in production (accuracy, latency, error rate).
  2. 2Detect data drift and model drift.
  3. 3Collect user feedback for continuous improvement.
  4. 4Set up alerts for performance degradation.
  5. 5Retrain the model periodically with new data.
Related13Monitoring, Logging and Observability15MLOps and LLMOps