What is a software bill of materials for an ML service?

A Software Bill of Materials (SBOM) is a structured inventory of the software components used to build an application—similar to an ingredients label on packaged food.

For an ML service, it helps engineers answer: Which Python libraries, operating-system packages, and other software dependencies are inside the version we deployed?

This matters because an ML model may work correctly while the software around it contains an outdated library, a problematic dependency, or a license the team needs to review.

A beginner-friendly example

Imagine a food-delivery company running an API that predicts how long an order will take.

The service uses a trained model plus Python, numpy, scikit-learn, a web framework, and packages inherited from its container's base image.

One day, the security team learns that a particular software library version may be vulnerable. Instead of asking every engineer to guess which projects use that library, they inspect the SBOM associated with the deployed prediction-service image.

The inventory can show whether that software component was discovered in the image and which version is present. This helps the team decide which releases need investigation.

What information can an SBOM contain?

Depending on the standard and generation tool, an SBOM can record:

• Component name and version: For example, the exact installed version of a Python dependency.
• Supplier or package identifiers: Information to distinguish similarly named packages.
• Dependency relationships: Which components depend on others.
• License information: Useful for reviewing software usage obligations.
• File or package hashes: When available, identifiers that help confirm the recorded artifact.
• SBOM metadata: Who or what generated it, when, and which software artifact was examined.

Two widely used machine-readable SBOM formats are SPDX and CycloneDX.

An SBOM is an inventory, not a report that every component is safe. Vulnerability tools can analyze the inventory against advisory data; assessing and fixing findings is a separate activity.

How can you generate one?

A team can generate an SBOM from a built container image, not just its source-code dependency declarations. This can capture software supplied by the base image as well as installed application packages.

For example, after building an image called delivery-time-api:1.0, a tool such as Syft can scan it:

BASH
syft scan delivery-time-api:1.0 -o cyclonedx-json=sbom.json

The command writes a machine-readable CycloneDX JSON inventory to sbom.json. It assumes Syft is installed and that the named image is accessible. Actual discovered components depend on the image contents and scanner coverage.

A common release workflow is:

1. Build the approved ML service image.
2. Generate its SBOM automatically in CI/CD.
3. Associate the SBOM with the exact image digest and release record.
4. Review newly discovered packages and relevant security or license findings.
5. Retain the SBOM with the release, and regenerate it when the image is rebuilt.

This is particularly useful when one organization has many training, batch-scoring, and inference images, each with a different software inventory.

Is an SBOM the same as a model card or dataset record?

No. A conventional software SBOM primarily describes software composition.

It does not automatically tell you:

• Which examples or datasets trained a model.
• Whether its predictions are accurate or fair.
• Which evaluation results justified approval.
• Whether a model file was replaced after approval.

Use model registries, model cards, dataset lineage, and artifact versioning for those questions. Some emerging AI-specific bills of materials cover additional AI assets, but a normal package-scanning SBOM should not be assumed to include them.

How does this connect to earlier MLOps concepts?

A dependency lockfile records a resolved set of packages the project intends to install. An SBOM inventories components found in a particular built or deployed artifact. They complement one another, but they are not interchangeable.

Likewise, a fixed container image digest helps tie the SBOM to a precise released artifact. If the image changes, the relevant SBOM should be updated.

In short: An SBOM is the software ingredients list for an ML service. Generate it from the release artifact, retain it with the release, and use it to understand dependencies and support software supply-chain reviews.