Tracking Model Artifacts Versions and Lineage

Design and implementation of artifact tracking and versioning is the system that logs, stores, versions, and links every machine learning (ML) artifact so any model can be reproduced and audited. In Google Cloud, Agent Platform ML Metadata (opens in a new tab) records lineage and relationships, Vertex AI Experiments logs parameters, metrics, and dataset and model linkages for each run, and Model Registry applies and manages immutable versions of each stored model. Together they provide traceability from raw data to prediction.

1.1Log Complete Lineage in ML Metadata and Experiments

Agent Platform ML Metadata is the lineage system that tracks models, datasets, and artifacts and the metadata about data sources, transformations, model parameters, and experiment results. Artifacts tracked in ML Metadata are automatically encrypted at rest, and ML Metadata also analyzes resources and runs across the entire MLOps pipeline. Data flows from data sources through transformations into training runs and then into model artifacts, and ML Metadata stores the link at each transition so the full path can be queried backward or forward.

Vertex AI Experiments is the run-tracking layer that integrates with Vertex ML Metadata to log and build linkage across parameters, metrics, and dataset and model artifacts. Experiments automatically logs training parameters, code versions, dataset versions, and evaluation results for each training and evaluation run, which creates a comparable history for debugging and reproducibility. Vertex AI TensorBoard visualizes those logged metrics, such as loss and accuracy over time, model graphs, and embeddings, to compare experiments by hyperparameters.

To create a strong data-to-model link, use managed datasets in Agent Platform:

• Managed datasets link custom-trained models directly to the dataset used for training
• Managed datasets provide descriptive statistics and automatic or manual splits into train, test, and validation sets
• Managed tabular datasets support artifact tracking when data is read through the BigQuery Storage API

Use a managed dataset when lineage between data and model is critical for audit or comparison, and keep splitting logic in training code only when explicit control outweighs the need for managed lineage.

1.2Apply Version Control by Artifact Type

Version control by artifact type assigns each kind of ML artifact to the store that provides its storage, versioning, and access control. Code versions flow from Git into builds, data versions flow from storage into training, and both flows converge in ML Metadata and Model Registry where pointers, parameters, and binaries are linked. Metadata and pointers to data locations are stored in the version control system or in a separate data catalog so the location and version can be resolved without copying the data.

For unstructured data on Cloud Storage, combine many images, video clips, or audio clips into large container files to improve read and write throughput, with a target of at least 100 MB per file and between 100 and 10,000 shards.

1.3Manage Model Versions in Model Registry

Model Registry is the central repository for model versions that tracks each version, its metadata, and its link to underlying model artifacts. Before a model is imported into Model Registry, model files and pretrained weights are stored in a service such as Cloud Storage and custom training or serving container images are stored in Artifact Registry. The registry then assigns the version and preserves the association between the versioned entry and those immutable binaries.

Each registered model version links to comprehensive provenance metadata:

• Training parameters and code version that produced the model
• Evaluation metrics from validation pipelines
• Dataset version used for training, with lineage traced back to the Knowledge Catalog entry
• Framework, foundation model, fine-tuning parameters, and for generative AI, quality and safety evaluation results

An automated registration lifecycle keeps the registry current without manual steps:

1. Store binaries: Training writes model files to Cloud Storage and images to Artifact Registry.
2. Register version: The pipeline calls the Agent Platform SDK or API to register the new version in Model Registry with a clear scheme such as semantic versioning.
3. Attach lineage: The registration links training parameters, evaluation metrics, dataset version, and code version in ML Metadata.
4. Control promotion: CI/CD pulls the versioned artifact from Artifact Registry and Model Registry for testing and deployment, with access policies enforced on both registries.

1.4Build End-to-End Traceability with Catalogs and Pipeline Lineage

End-to-end traceability connects enterprise data discovery, pipeline execution, and full MLOps lineage into one auditable path. Knowledge Catalog builds the enterprise-wide catalog by automatically discovering inventories from BigQuery, Cloud Storage, and Pub/Sub, and it tracks the lineage of pipeline artifacts from Agent Platform Pipelines and supports search for model and dataset resources. Tag and annotate datasets in the catalog with business metadata, ownership, sensitivity level, and lineage details, such as linking a processed dataset to its raw source and to the pipeline that created it.

Choose the lineage scope by the task. Track individual pipeline artifacts in Knowledge Catalog when the goal is debugging, troubleshooting, or root cause analysis of a single step. Track the entire MLOps pipeline, including the lineage of all pipeline artifacts, in Agent Platform ML Metadata when the goal is full audit, reproducibility, or analysis of resources and runs. Implement automated processes inside MLOps pipelines so data and model assets are registered and updated in their catalogs upon creation or modification, which preserves the chain from raw data to prediction even as teams and projects change.

1.5Enforce Immutability, Security, and Auditability

Immutability, security, and auditability ensure that a tracked version cannot be silently altered and that every access or change is recorded. Encrypt model artifacts and sensitive files with Cloud Key Management Service (Cloud KMS) to protect data at rest and in transit, store files in Cloud Storage with configured access controls, and store model artifacts in Model Registry to gain fine-grained access control and encryption at rest. Create digital signatures for packages and containers produced during approved builds, and store data in Cloud Storage multi-region or dual-region buckets so objects are redundantly stored across at least two geographic locations for durability against regional outages.

Access and audit controls bound who can create or change versions and who can see the trail. The control plane depends on Identity and Access Management (IAM) for central management of users, groups, and service accounts, with dedicated least-privilege service accounts for pipelines — for example, a training service account that can read only one Cloud Storage bucket and write only to Model Registry — and IAM Conditions for attribute-based control, such as allowing a pipeline trigger only from a trusted Cloud Build trigger. Enable audit logs for Agent Platform to track API calls and administrative actions such as endpoint creation, model deployment, and configuration updates, analyze access in Observability Analytics to determine who accessed or modified data and models and when, stream logs to Cloud Logging for real-time analysis, export to BigQuery for long-term retention, and route to security information and event management (SIEM) or third-party destinations for centralized monitoring.

A model lineage graph is a connected record that traces a model artifact backward to its originating dataset and pipeline and forward to its deployed predictions. In Google Cloud, Agent Platform ML Metadata (opens in a new tab) provides the graph-based lineage for this record, linking data sources through transformations and training runs to versioned model artifacts. The graph enables impact analysis, regulatory compliance, and root-cause debugging because every edge names both its source and its destination.

2.1What a lineage graph records

A lineage graph records how raw data becomes a prediction through a chain of artifacts, transformations, and runs. Agent Platform ML Metadata stores lineage for models, datasets, and artifacts that are automatically encrypted at rest, including logged metadata about data sources, transformations, model parameters, and experiment results. The service also records resources and runs from Agent Platform Pipelines, so a training run connects its input dataset version and code version to its output model artifact and evaluation metrics. When evaluation results from validation pipelines are logged to Agent Platform ML Metadata, the graph connects model quality evidence directly to the model version that produced it.

The complete chain flows from raw data sources to processed datasets to trained model versions to deployed endpoints and predictions. A processed dataset points back to its raw source and to the pipeline that created it, a training run points back to its dataset version and code version, and a registered model version points back to its training run and forward to its endpoint deployment. This end-to-end traceability from raw data to prediction is what lets teams audit the exact inputs and processes that led to a specific model version or prediction.

2.2Constructing complete lineage across the MLOps pipeline

Constructing complete lineage means automatically registering every asset and linking catalog entries across services during pipeline execution. Automated processes inside the MLOps pipeline ensure that data and model assets are registered and updated in their respective catalogs upon creation or modification, rather than relying on manual entry. Datasets are tagged with business metadata, ownership information, sensitivity levels, and lineage details, and each processed dataset is linked to its raw source and creating pipeline. Model artifacts such as model files and pretrained weights are stored in Cloud Storage before import, while custom container images for serving or training are stored in Artifact Registry, and then each trained model is registered with its full context.

Each entry in Model Registry on Gemini Enterprise Agent Platform applies and manages the version of the stored model and links that version to its provenance metadata:

• training parameters and code version that produced the dataset and model
• dataset version used for training, with lineage traced back to the relevant Knowledge Catalog entry
• evaluation metrics from validation pipelines and details about the framework or foundation model used
• for generative AI and foundation models, fine-tuning parameters and evaluation results specific to output quality and safety
• storage pointers to underlying artifacts in Cloud Storage or Artifact Registry with a clear versioning scheme such as semantic versioning

2.3Interpreting lineage for debugging and impact analysis

Interpreting lineage means traversing the graph in two directions to answer governance and debugging questions. Backward traversal starts from a faulty prediction or model version and follows edges back to the cause, while forward traversal starts from a changed or suspect dataset and follows edges forward to all affected models. Knowledge Catalog tracks the lineage of pipeline artifacts from Agent Platform Pipelines and supports search for model and dataset resources, which makes it effective for inspecting an individual pipeline artifact during debugging, troubleshooting, or root-cause analysis. Agent Platform ML Metadata tracks the entire MLOps pipeline, including the lineage of those pipeline artifacts, which makes it effective for analyzing resources and runs across the full lifecycle.

A backward root-cause walk follows an explicit order. First, identify the deployed model version in Model Registry that served the bad prediction. Then, follow the edge from that model version to its training and evaluation run in Agent Platform ML Metadata to review parameters and metrics. Then, follow the edge from the run to its dataset version and back to the raw source and transformation in Knowledge Catalog to check for drift, bias, or upstream change. A forward impact walk reverses the direction: start from a changed raw source or processed dataset, find all downstream training runs that consumed that dataset version, then list all registered model versions that depend on those runs to decide what must be retrained, re-evaluated, or rolled back.

2.4Comparing lineage and governance surfaces

The lineage and governance surfaces divide work by scope, with each service depending on the others for a complete record. Agent Platform ML Metadata depends on pipeline runs to supply lineage edges, Model Registry depends on Agent Platform ML Metadata and Knowledge Catalog entries to supply version context, and Knowledge Catalog depends on storage systems for discovery. Audit coverage depends on Cloud Audit Logs for Agent Platform to record who accessed or modified data and models and when.

After the catalog choice is made, governance depends on keeping the links intact over time. Because Model Registry versions point to dataset versions that point to Knowledge Catalog entries, an auditor can move from a deployed endpoint back to training data without gaps. Because audit logs can be analyzed in Observability Analytics, exported to BigQuery for long-term retention, or routed to third-party destinations and security information and event management (SIEM) systems, access evidence complements lineage evidence for compliance and misuse investigation.

Analyzing and comparing experiment runs is the practice of building many model variants and contrasting their metrics, hyperparameters, and artifacts to identify the optimal model. On Google Cloud, Vertex AI TensorBoard provides visual, collaborative comparison of runs, while Experiments on Agent Platform provides linked tracking of parameters, metrics, and artifacts through lineage. Together they let teams working on Agent Platform and Gemini Enterprise Agent Platform reproduce results, share findings, and govern models.

3.1Comparing Many Candidate Models to Find What Works Best

Comparison of candidate models starts from the need to build and test many variants because no single configuration is known to work best in advance. Data scientists vary the inputs that change model behavior to create a comparison set:

• architectures that define the model structure
• input datasets that define the training data
• hyperparameters that define the training configuration
• hardware that defines the compute used for training

Each run in the comparison set produces aggregate performance metrics such as accuracy, precision, and recall on test datasets. The final selection then contrasts those aggregates against performance on particular data subsets, against different model versions, and against different model architectures, so the optimal model is chosen for both overall quality and behavior on important slices.

3.2Vertex AI TensorBoard for Visual and Collaborative Comparison

Vertex AI TensorBoard is an enterprise-ready managed service for visualizing and comparing specific experiments, for example experiments that differ by hyperparameters. The service tracks experiment metrics like loss and accuracy over time, visualizes the model graph, and projects embeddings to a lower dimensional space. Because the service is managed, cost-effective, and secure, data scientists and ML researchers can track, compare, and share experiments without operating their own tracking infrastructure.

Data flows from training jobs into Vertex AI TensorBoard, where time-series metrics, graphs, and embeddings from different runs appear side by side in the same view. This shared view lets teams across projects compare hyperparameter choices and training dynamics directly, rather than exchanging isolated charts. Choose Vertex AI TensorBoard when the question is how training behaved over time and how visual differences in curves, graphs, or embeddings explain the gap between runs.

3.3Experiments on Agent Platform for Linked Parameter and Artifact Tracking

Experiments on Agent Platform is the tracking capability that logs runs and builds linkage across parameters, metrics, and dataset and model artifacts. An experiment run records the hyperparameters used, the metrics produced, and pointers to the dataset artifact that trained it and the model artifact it produced. The capability depends on Vertex ML Metadata (opens in a new tab) for storage and lineage, so every logged link becomes part of a queryable history of what produced what.

This linkage is what makes comparison reproducible across teams and across Agent Platform and Gemini Enterprise Agent Platform. When two teams report different accuracy numbers, their experiment records show whether the difference came from parameters, data, or code lineage rather than from an undocumented change. Choose Experiments on Agent Platform when the question requires provenance, such as which dataset version plus which parameters produced a given model artifact.

3.4Vertex ML Metadata and Model Registry for Cross-Team Lineage

Vertex ML Metadata is the lineage system that records metadata about data sources, transformations, model parameters, and experiment results, with artifacts automatically encrypted at rest. Metadata flows from pipeline steps, training jobs, and experiment logging into Vertex ML Metadata, where the service links pipeline artifacts to the runs and resources that created them. The service also supports analysis of resources and runs across the entire MLOps pipeline, so a team can trace a deployed model back through its training run to its source data.

Two companion systems extend that lineage for collaboration. Knowledge Catalog searches for model and dataset resources and tracks the lineage of pipeline artifacts from Agent Platform Pipelines, which supports debugging, troubleshooting, and root-cause analysis of an individual artifact. Model Registry on Gemini Enterprise Agent Platform (Model Registry) applies and manages versions of each stored model, so comparison across teams resolves to explicit versioned artifacts rather than ambiguous files. Administrative accountability sits outside the artifact graph: audit logs for Agent Platform record API calls and administrative actions, and Observability Analytics analyzes those logs to show who accessed or modified data and models and when.

3.5Hyperparameter-Driven Comparison With Managed Training

Hyperparameter-driven comparison is the systematic testing of different hyperparameter configurations to maximize predictive accuracy. The Gemini Enterprise Agent Platform Managed Training service (Managed Training) runs the training jobs on Google Cloud processing infrastructure, while Vertex AI Vizier (Vertex AI Vizier) selects and tests configurations across numerous training runs. Results from each configuration flow back into Experiments and Vertex AI TensorBoard as a comparable run with its own parameters, metrics, and artifacts.

This automation removes the need to manually adjust hyperparameters over many runs to arrive at optimal values. Because every Vizier trial is logged with the same parameter-metric-artifact linkage, teams can contrast trials across runs, teams, and platforms and promote only the configuration with verified lineage. Choose managed hyperparameter tuning when the comparison requires many controlled trials that must remain reproducible and shareable rather than ad hoc notebook runs.

Key Facts

• Agent Platform ML Metadata stores graph-based lineage linking data sources through transformations and training runs to versioned model artifacts, while Vertex AI Experiments logs parameters, metrics, and dataset and model linkages for each run.
• Model Registry on Gemini Enterprise Agent Platform stores, organizes, and versions model versions and links each version to training parameters, code version, dataset version, and evaluation metrics.
• Knowledge Catalog tracks lineage of individual pipeline artifacts from Agent Platform Pipelines for debugging, while Agent Platform ML Metadata tracks the entire MLOps pipeline for audit and reproducibility.
• Vertex AI TensorBoard visualizes loss and accuracy over time, model graphs, and embeddings for visual comparison, while Experiments on Agent Platform links parameters, metrics, and artifacts through Vertex ML Metadata for reproducible comparison.