Building a Basic ML Pipeline

A machine learning pipeline assembles individual automation principles like Continuous Integration (CI), Continuous Delivery (CD), and Continuous Training (CT) into a cohesive, automated workflow. This workflow forms the backbone of a reliable MLOps system, transforming a sequence of manual, error-prone tasks into a repeatable and auditable process.

Think of an ML pipeline as an automated assembly line for your models. It takes raw materials like data and code at one end and produces a validated, ready-to-deploy model at the other. Each step in the pipeline is a distinct, automated task that passes its output, known as an artifact, to the next step.

The Stages of a Basic Pipeline

A typical ML pipeline automates the core stages of the machine learning lifecycle. While pipelines can become very complex, a basic one includes the following automated stages:

Data Ingestion: This is the entry point. The pipeline automatically pulls in the required data. This could be from a data warehouse, a cloud storage bucket, or a feature store. The trigger for this step is often the availability of new data.
Data Validation: Once data is ingested, it must be validated. The pipeline automatically checks the data for quality issues. Does it match the expected schema? Are there missing values or anomalies? This step prevents low-quality data from corrupting the entire training process. If validation fails, the pipeline can halt and send an alert.
Data Preparation: This stage, also known as feature engineering, automatically transforms the validated raw data into a format suitable for the model. This includes tasks like scaling numerical features, encoding categorical variables, and creating new features from existing ones. The output is a processed dataset ready for training.
Model Training: The pipeline executes the training script using the prepared data. This script trains the model and outputs a trained model file, which is a binary artifact. Importantly, the pipeline also logs all the parameters and environment details used for the training run to ensure reproducibility.
Model Evaluation: After training, the model must be evaluated. The pipeline automatically tests the new model's performance on a held-out test set. It calculates predefined metrics, such as accuracy, precision, or Mean Squared Error. The results are compared against a baseline, which could be the currently deployed model or a minimum performance threshold (e.g., accuracy > 0.85).
Model Registration: If the model passes the evaluation stage, it is registered. This step involves versioning the model artifact and storing it in a central location called a Model Registry. The registry also stores important metadata, such as the training metrics, the ID of the dataset used, and a link to the code version that produced it. A registered model is a candidate for deployment.

A diagram of a basic automated machine learning pipeline, showing the progression from data ingestion to model registration.

How Pipelines are Triggered

An automated pipeline is not run manually. It is activated by specific events, ensuring the system responds dynamically to changes. Common triggers include:

Code-based Triggers (CI): When a data scientist or engineer pushes new code to a Git repository, a CI system (like GitHub Actions) can automatically trigger a pipeline run. This pipeline typically runs a series of quick tests to validate the code's integrity and might execute a short training run to ensure nothing is broken.
Data-based Triggers (CT): In many applications, models need to be retrained as new data becomes available. A pipeline can be configured to start automatically when a certain amount of new data has been collected in storage. This is the essence of Continuous Training (CT).
Scheduled Triggers: Sometimes, you need to retrain a model on a regular cadence regardless of new data, perhaps to combat slow-changing trends or model staleness. A pipeline can be scheduled to run at fixed intervals, such as daily, weekly, or monthly.

By combining these components and triggers, you create a system that is no longer a static, one-off script. Instead, it becomes a dynamic, event-driven process that reliably and repeatedly produces high-quality models. This pipeline is the engine that drives MLOps, bridging the gap between model development and operational excellence. The output of this pipeline, a versioned and validated model in a registry, becomes the input for the next phase: Continuous Delivery.

Was this section helpful?

References

Introducing MLOps, Mark Treveil, Nicolas Omont, Clément Stenac, Kenji Lefevre, Du Phan, Joachim Zentici, Adrien Lavoillotte, Makoto Miyazaki, Lynn Heidmann, 2020 (O'Reilly Media) - A book that provides an introduction to MLOps principles, including machine learning pipelines, continuous integration, continuous delivery, and continuous training.
Vertex AI Pipelines overview, Google Cloud Documentation, 2024 (Google Cloud) - Official documentation for Google Cloud's Vertex AI Pipelines, which illustrates how a production-grade ML pipeline platform implements the stages and concepts discussed.
Hidden Technical Debt in Machine Learning Systems, D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison, 2015 Advances in Neural Information Processing Systems 28 (Curran Associates, Inc.) - A seminal paper that identifies the challenges and costs in deploying and maintaining machine learning systems, providing the rationale for robust MLOps practices like automated pipelines.