Saving and loading models

Training a machine learning model is often the most expensive step in a program’s lifecycle. Once a model has been trained, we usually want to keep it and reuse it rather than repeat that work every time the program runs. Persisting trained models is therefore a practical necessity for real AI-enabled systems, especially when models are used repeatedly inside agents or long-running workflows.

Why trained models must be persisted

A trained model represents learned information derived from data. That information lives in memory while a program is running, but it disappears when the program exits. Persisting a model allows us to separate the costly training step from later prediction and decision-making steps.

This also lets us treat a model as an artifact. It can be versioned, shared, deployed, and reused just like data files or generated outputs.

Saving a trained model to disk

In Python, trained scikit-learn models can be serialized and written to disk using standard tools. The most common approach is to use joblib, which is designed for efficiently saving objects that contain large numerical data.

A trained model can be saved once training has completed.

import joblib

joblib.dump(model, "planet_classifier.joblib")

This writes the model’s internal state to a file. The program can then exit without losing the trained parameters.

Loading a saved model back into memory

A saved model can be loaded back into memory in a later program run. Once loaded, the model behaves exactly like the original trained object.

import joblib

model = joblib.load("planet_classifier.joblib")

After loading, the model is ready to receive input data and produce predictions without retraining.

Managing model files alongside code and data

Model files are typically stored alongside code and datasets in a project’s directory structure. They are treated as inputs to inference-time programs, not as source code.

Keeping model files clearly named and intentionally placed makes it obvious which version of a model a program is using. This becomes especially important when multiple models or training runs are involved.

Reusing models across program runs

Once models are saved and loaded reliably, they can be reused across many executions of a program. Training can happen once, while prediction and decision logic can run repeatedly.

This separation is foundational for agent systems. It allows learning to be performed offline, while agents operate efficiently and predictably using pre-trained models.

Conclusion

By saving and loading trained models, we turn machine learning results into durable program components. The model no longer lives only inside a single run but becomes a reusable artifact. At this point, we are equipped to treat trained models as stable computational tools that can be loaded on demand and used wherever predictions are needed.