How to Build End-to-End Machine Learning Lineage: Complete Tutorial
By Braincuber Team
Published on March 5, 2026
A model that worked perfectly last Tuesday is now returning garbage predictions. Your team scrambles. Was it the data? The preprocessing? A config change someone pushed without telling anyone? Without ML lineage, you're debugging blind. Machine learning lineage tracks the complete lifecycle of a model — code, data, experiments, model artifacts, and predictions — so you can trace back exactly what changed and when. It's the difference between a 4-hour root cause investigation and a 4-week blame game. This complete tutorial walks you through building end-to-end ML lineage using DVC, AWS S3, Evidently AI, and Prefect — with a real AI pricing system for retailers as the use case.
What You'll Learn:
- How to initiate a DVC project for version-controlled ML pipelines
- How to build 6 lineage stages: ETL, data drift, preprocessing, tuning, inference, and fairness
- How to deploy DVC cache to AWS S3 for production access
- How to configure Prefect for scheduled pipeline orchestration
- How to use Evidently AI for data drift detection
- How to assess model risk and fairness before production deployment
- How to deploy the complete application with Docker and AWS Lambda
Why ML Lineage Isn't Optional Anymore
ML lineage is a framework for tracking and understanding the complete lifecycle of a machine learning model. It tracks information at multiple levels: code (scripts, libraries, configurations), data (original data, transformations, features), experiments (training runs, hyperparameter results), models (trained artifacts and versions), and predictions (outputs of deployed models).
Reproducibility
Recreate the same model and prediction for validation. When the auditor asks "how did you arrive at this prediction?" — you have the exact data, code, and config that produced it.
Root Cause Analysis
Trace back to the data, code, or configuration change when a model fails in production. Without lineage, you're guessing which of 47 possible variables caused the failure.
Compliance
Regulated industries require proof of model training to ensure fairness, transparency, and adherence to laws like GDPR and the EU AI Act. Lineage gives you the audit trail.
The Architecture: AI Pricing for Retailers
The system we're building operates as a containerized, serverless microservice that provides optimal price recommendations to maximize retailer sales. Its AI models are trained on historical purchase data to predict quantity sold at various prices — allowing sellers to determine the best price point.
The prediction logic and dependencies are packaged into a Docker container image stored in AWS ECR. An AWS Lambda function retrieves and runs the container, exposing results via API Gateway for a Flask application to consume. GitHub handles code lineage, while DVC captures the lineage of data, experiments, and models.
Step by Step: Building the ML Lineage Pipeline
Initiate a DVC Project
Run dvc init at the root of your project. This creates a .dvc directory containing cache, config, and temporary files. DVC separates large data files from Git by caching originals locally, creating small .dvc metadata files with MD5 hashes, pushing only metadata to Git, and pushing original data to a DVC remote (AWS S3). This keeps your Git repo fast and lightweight while tracking every data version.
Build the ETL Pipeline Stage
Define the first lineage stage in dvc.yaml: etl_pipeline. This stage extracts raw data, cleans it, imputes missing values, and performs feature engineering. Each stage in DVC requires a command definition in dvc.yaml and a corresponding Python script. DVC tracks inputs (deps), outputs (outs), metrics, and parameters for each stage — creating a complete dependency graph that knows when to re-run based on changes.
Add Data Drift Detection with Evidently AI
The data_drift_check stage uses Evidently AI to detect shifts in data distributions that could compromise model performance. If drift tests fail, the system exits immediately — preventing a degraded model from reaching production. This stage compares current data distributions against reference data, generating reports that document exactly what changed and by how much. Critical for catching data pipeline issues before they corrupt your model.
Configure Preprocessing and Model Tuning
Two DVC stages handle this: preprocess (creates training, validation, and test datasets) and tune_primary_model (hyperparameter tuning and model training). DVC tracks every experiment — the hyperparameters used, the metrics produced, the model artifacts generated. Each run gets an MD5 or SHA256 hash, so you can compare any two experiments and see exactly what differed. No more "which Jupyter notebook had the good results?" chaos.
Run Inference and Fairness Testing
The inference_primary_model stage performs inference on the test dataset, and the assess_model_risk stage runs risk and fairness tests. Only models that pass both data drift and fairness tests can serve predictions via the AWS API Gateway. This gate prevents biased or degraded models from reaching customers — a compliance requirement under GDPR and the EU AI Act. DVC logs the test results as metrics for every pipeline run.
Deploy DVC Cache to AWS S3
Create an S3 bucket, add the URI to the DVC remote with dvc remote add, then push cache files with dvc push. Ensure your IAM role has s3:ListBucket, s3:GetObject, s3:PutObject, and s3:DeleteObject permissions. This deployment step is essential — your AWS Lambda function needs access to the cached files in production. DVC supports multiple storage backends (Google Cloud, Azure), but S3 integrates natively with the Lambda architecture.
Configure Scheduled Orchestration with Prefect
Prefect is an open-source workflow orchestration tool for building, scheduling, and monitoring pipelines. It uses a work pool concept to decouple orchestration logic from execution infrastructure, running a Docker container image for consistent environments. Configure Prefect to trigger the entire lineage process on a weekly schedule — Prefect prompts DVC to check for updates in data and scripts, executing the full lineage only if changes are detected. Set up the Docker image registry, define Prefect tasks and flows, then deploy.
| Lineage Stage | DVC Stage Name | What It Tracks | Tool Used |
|---|---|---|---|
| Data Extraction | etl_pipeline | Raw data, cleaned data, features | DVC + Python |
| Drift Detection | data_drift_check | Distribution shifts, drift reports | Evidently AI |
| Preprocessing | preprocess | Train/val/test splits | DVC + Python |
| Model Tuning | tune_primary_model | Hyperparameters, metrics, artifacts | DVC + Python |
| Inference | inference_primary_model | Predictions, performance metrics | DVC + Python |
| Fairness Testing | assess_model_risk | Risk scores, fairness reports | DVC + Python |
# dvc.yaml — ML Lineage Pipeline
stages:
etl_pipeline:
cmd: python src/etl_pipeline.py
deps:
- src/etl_pipeline.py
- data/raw/
outs:
- data/processed/features.csv
data_drift_check:
cmd: python src/data_drift_check.py
deps:
- data/processed/features.csv
- data/reference/
metrics:
- reports/drift_report.json
preprocess:
cmd: python src/preprocess.py
deps:
- data/processed/features.csv
outs:
- data/splits/train.csv
- data/splits/val.csv
- data/splits/test.csv
tune_primary_model:
cmd: python src/tune_model.py
deps:
- data/splits/train.csv
- data/splits/val.csv
outs:
- models/primary_model.pkl
metrics:
- reports/tuning_metrics.json
params:
- params.yaml
inference_primary_model:
cmd: python src/inference.py
deps:
- models/primary_model.pkl
- data/splits/test.csv
outs:
- predictions/output.csv
metrics:
- reports/inference_metrics.json
assess_model_risk:
cmd: python src/assess_risk.py
deps:
- predictions/output.csv
metrics:
- reports/fairness_report.json
Initial Planning Matters
Defining how metrics are tracked and at which stages leads directly to a cleaner, more maintainable code structure. Skipping the planning step means you'll retrofit lineage tracking into an existing pipeline — which is 3-4x more painful than building it in from the start. Map your stages, define your metrics, and design your dependency graph before writing a single line of pipeline code.
Frequently Asked Questions
What is machine learning lineage?
ML lineage is a framework for tracking the complete lifecycle of a machine learning model — code, data, experiments, model artifacts, and predictions. It ensures reproducibility, enables root cause analysis when models fail, and provides compliance audit trails for regulations like GDPR and the EU AI Act.
What tools are used to build ML lineage in this tutorial?
DVC (Data Version Control) tracks the ML lineage, AWS S3 serves as remote storage, Evidently AI handles data drift detection, and Prefect manages scheduled orchestration. The application itself runs on AWS Lambda with Docker containers stored in AWS ECR.
Why is data drift detection important in ML pipelines?
Data drift occurs when the distribution of incoming data changes from the data used to train the model. Without detection, your model silently degrades in production — returning increasingly inaccurate predictions without any alert. Evidently AI catches these shifts before they compromise the model's real-world performance.
How does DVC track data and model versions?
DVC uses MD5 or SHA256 hashes to create small metadata files that reference large data and model files. Only the metadata is pushed to Git, while actual data goes to a remote storage (like AWS S3). This keeps your Git repo lightweight while maintaining complete version history of every artifact.
Can I use this lineage approach without AWS?
Yes. DVC supports multiple storage backends including Google Cloud Storage, Azure Blob Storage, and local storage. Prefect runs anywhere Python runs. The lineage architecture is cloud-agnostic — only the deployment target (Lambda, ECR) is AWS-specific, and those can be swapped for equivalent services on other clouds.
Running ML Models Without Lineage?
We'll audit your ML pipeline, implement end-to-end lineage tracking with DVC and Prefect, set up data drift monitoring, and build the compliance audit trails your regulated industry demands. Stop debugging blind. Start tracing every prediction back to its source.