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MLOps Pipeline

MLOps Step 3: ML Model Training

● Intermediate ⏱ 45 min read MLOps Pipeline

Training a model is not a one-time event — it's a repeatable process that happens every time your data changes, your code improves, or you want to try a different approach. This guide focuses on making training reproducible, tracked, and deployable to Kubernetes at scale.

The Training Loop

Every ML training run follows the same pattern: load data, define model, train iteratively, evaluate, save. The specifics differ by model type, but the structure is universal.

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MLflow Experiment Tracking

MLflow is the de facto standard for tracking ML experiments. It stores every run's parameters, metrics, and artifacts in a queryable database, so you can compare runs and reproduce any result.

MLflow Components

  • Tracking Server: Stores experiment metadata (params, metrics). Backed by PostgreSQL in production.
  • Artifact Store: Stores large files (model binaries, plots). Backed by S3.
  • Model Registry: Versioned model catalog with stage transitions (Staging → Production).
  • UI: Web interface for comparing runs, viewing metrics, and downloading artifacts.
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Hyperparameter Tuning with Optuna

Manual hyperparameter search doesn't scale. Optuna is a modern, efficient hyperparameter optimization framework that uses Bayesian optimization to find good parameters much faster than grid search.

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When to Tune Hyperparameters Hyperparameter tuning has diminishing returns. Spend 80% of your time on data quality and feature engineering, 20% on tuning. A well-tuned model on bad features will underperform a default model on good features.

Model Evaluation and Promotion

Before promoting a model to production, you need an automated evaluation gate that checks it meets minimum performance thresholds — and that it's better than the current production model.

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Running Training on Kubernetes

For large models or datasets that won't fit on a single machine, you need to run training as a Kubernetes Job. This gives you access to GPU nodes, large memory instances, and automatic cleanup.

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