MLOps Pipeline from Scratch (2026): The Full End‑to‑End Workflow
Most beginners can train a model in a notebook. The hard part is building a system that can be retrained, deployed, monitored, and audited over time. That is what MLOps (Machine Learning Operations) is about.
This guide explains the full MLOps pipeline from scratch. It’s written so that a student can read it end‑to‑end and understand how real ML systems work in production.
The MLOps Pipeline at a Glance
- Data: collect, validate, label, and version it
- Features: define transformations consistently for training and serving
- Training: reproducible training code + configs
- Experiment tracking: metrics, parameters, artifacts
- Evaluation gates: tests for model quality, bias/safety, and latency
- Model registry: version models and promote stages (staging → production)
- Deployment: batch, online API, or edge/on-device
- Monitoring: performance, drift, data quality, cost
- Retraining: scheduled or triggered when drift is detected
1) Data: Collection, Validation, and Versioning
In production, data is your most important dependency. You want to answer: “Which exact data created this model?”
- Data contracts: expected columns, types, ranges, and missing value rules.
- Validation: schema checks, anomaly detection, and label sanity checks.
- Versioning: keep dataset snapshots so training is reproducible (DVC/lakehouse snapshots are common patterns).
2) Feature Engineering and Feature Stores
A classic production bug is “training‑serving skew”: features are computed one way in training and a different way in production. A feature store helps you define features once and serve them consistently.
- Offline store: compute features for training.
- Online store: serve the same features for real‑time predictions.
- Monitoring: track feature distributions and anomalies.
3) Training: Reproducible Runs (Not “Notebook Magic”)
Training should run from a script with config files. A clean setup includes:
- Fixed random seeds (where possible)
- Dependency pinning (requirements lockfile)
- Config-driven hyperparameters
- Artifacts: model file, tokenizer, preprocessing code
4) Experiment Tracking (Why MLflow Is Everywhere)
In MLOps, you must be able to compare runs. Tracking tools record:
- parameters (learning rate, model type, feature set)
- metrics (accuracy, F1, AUC, RMSE, latency)
- artifacts (model, plots, confusion matrix)
If your team can’t answer “Which run is in production?”, you don’t have MLOps yet.
5) Evaluation Gates: Quality, Safety, and Cost
Before deployment, production pipelines use “gates” (checks that must pass):
- Model quality: on a fixed test set + slices (e.g. different user groups).
- Robustness: performance under noisy inputs.
- Bias/safety: unacceptable behaviour checks.
- Latency and cost: inference time and compute budget.
6) Model Registry and Promotion (Staging → Production)
A model registry stores versions and metadata. You typically promote a model through stages:
- Dev: early experiments, unstable.
- Staging: candidate model with full evaluation.
- Production: actively serving users.
7) Deployment: Batch vs Online vs Edge
Deployment is not one thing:
- Batch: run predictions nightly and write to a table (cheap and simple).
- Online: real-time API (fast, more engineering).
- Edge/on-device: privacy + low latency, but tight resource constraints.
For online deployments, containerization (Docker) and orchestration (Kubernetes) are common, but managed platforms like Vertex AI and SageMaker simplify operations for teams.
8) Monitoring: Accuracy Is Not Enough
After deployment, models degrade because the real world changes. Monitoring usually includes:
- Data drift: input distributions change.
- Prediction drift: output distributions change.
- Performance: if labels arrive later, measure real accuracy over time.
- System metrics: latency, errors, CPU/GPU, cost.
9) Retraining: Scheduled or Triggered
Retraining isn’t “train again sometimes.” It should be a repeatable pipeline:
- Scheduled: weekly/monthly retrains on new data.
- Triggered: retrain when drift thresholds or KPI drops are detected.
- Safe rollout: A/B tests, canary releases, and easy rollback to last good model.
Where RAG and “LLMOps” Fit
If you’re building LLM apps, the same ideas apply, but you also version prompts, retrieval configs, and evaluation sets. If you’re curious, start with our RAG posts:
Want to learn MLOps with a mentor?
At Paath.online, we teach Python → ML → deployment step-by-step with real projects, so students understand how models work in production.