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How to Chain ML Models Into a Pipeline

An ML pipeline chains multiple machine learning steps together so the output of one model becomes the input for the next. This lets you build complex analysis flows, like first clustering customers into segments and then running a churn prediction model on each segment separately, all without writing code or manually passing data between steps.

What Is an ML Pipeline

A pipeline is a sequence of data processing and modeling steps that run in order. In traditional data science, pipelines are built with code libraries like scikit-learn or Apache Spark. On a no-code platform, you define the same sequence visually by selecting which models to run and how data flows between them.

The simplest pipeline has two stages: a preprocessing step that cleans or transforms data, followed by a prediction step. More advanced pipelines might include feature engineering, multiple model predictions, and a final aggregation step that combines results. Each stage operates on the output of the previous one, creating a repeatable process you can run on new data at any time.

Why Use Pipelines Instead of Individual Models

Running models independently works fine when you have a single question to answer. But real business problems often involve multiple layers of analysis. Consider these examples:

Segmented prediction: First cluster customers into groups using clustering, then run separate churn prediction models tuned to each segment. High-value customers and budget customers churn for different reasons, so one universal model misses important patterns.
Anomaly filtering: First run anomaly detection to flag unusual data points, then run your prediction model on the clean data. This prevents outliers from skewing results.
Multi-stage scoring: First predict likelihood to purchase, then predict expected purchase amount, then multiply them together for an expected revenue score per lead. This gives your sales team a single number to prioritize by.
Feature enrichment: First run a classification model that categorizes raw text data (like support tickets into topic categories), then use those categories as input features for a second model that predicts resolution time.

Without pipelines, you would need to export results from one model, reformat the data, and manually upload it to the next model. Pipelines automate this entire handoff.

How to Build a Pipeline

Step 1: Train each model individually first.
Before chaining models, train each one separately and verify that it produces good results on its own. Follow the model training guide for each model in your planned pipeline. Make sure each model's output makes sense before connecting them.

Step 2: Define the pipeline order.
Decide which model runs first and what data it passes to the next step. The output columns of model A need to match what model B expects as input. If model A produces a "customer_segment" column, model B should be trained to use that column as an input feature.

Step 3: Connect the models in the Data Aggregator.
In the Data Aggregator app, create a pipeline configuration that specifies the model sequence. Select which output columns carry forward to the next stage and which are dropped. The platform handles data format conversion between steps automatically.

Step 4: Test the pipeline with sample data.
Run a small batch of data through the full pipeline and verify that each stage produces expected results. Check that the final output contains all the columns you need for your business decision.

Step 5: Run the full pipeline on production data.
Upload your complete dataset and execute the pipeline. All stages run in sequence automatically. The final output contains predictions from every stage, so you can see both intermediate results and final scores.

Common Pipeline Patterns

Filter Then Predict

Use anomaly detection as the first stage to remove outlier records, then feed the clean data into a classifier or regressor. This is especially useful when your training data contains noisy entries that could distort the main model's accuracy.

Segment Then Specialize

Use clustering to divide records into natural groups, then apply a different trained model to each group. This works well when different segments of your data behave differently. Customer behavior in different industries, product performance across regions, or patient outcomes by age group all benefit from this approach.

Predict Then Decide

Run a prediction model to score each record, then apply threshold rules that route records into action categories. Leads scoring above 0.8 go to the sales team immediately. Leads between 0.5 and 0.8 enter a nurture drip campaign. Leads below 0.5 receive a general newsletter.

Cost note: Pipeline costs are the sum of each individual model's training cost. Predictions remain free after training for every model in the chain, so running the pipeline on new data costs nothing regardless of how many stages it includes.

When Pipelines Are Overkill

Not every prediction problem needs a pipeline. If a single model achieves acceptable accuracy on its own, adding pipeline complexity does not help. Start simple, measure results, and only add pipeline stages when you have a clear reason, like segment-specific accuracy improvements or the need to combine predictions from different model types. The best pipeline is the simplest one that solves your problem.

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