ML Metadata Store Solutions

Neptune is a metadata store purpose-built for MLOps, designed for research and production teams that run a large volume of experiments. It provides a unified workspace to log, compare, and visualize experiment parameters, metrics, model checkpoints, and output artifacts, enabling teams to rapidly identify the best-performing configurations and reproduce any past run.

• Real-time experiment tracking and comparison dashboards
• Integrates with 50+ ML frameworks and libraries
• Collaborative workspace for research and production teams

TensorFlow ML Metadata (MLMD) is a library for recording and retrieving metadata associated with ML developer and data scientist workflows. As part of the TFX (TensorFlow Extended) ecosystem, MLMD uses a graph-based data model to capture artifacts, executions, and contexts — providing a complete lineage trail from raw data ingestion through model serving, essential for pipeline debugging and regulatory auditing.

• Graph-based metadata model with full artifact lineage
• Part of the TFX production ML pipeline framework
• Supports multiple backends including SQLite and gRPC

Google Cloud Vertex ML Metadata captures your ML system's metadata as a graph, providing a fully managed service for tracking artifacts, executions, and contexts within the Vertex AI platform. It leverages the open-source ML Metadata library under the hood while adding cloud-native features like IAM integration, regional storage, and seamless connectivity with Vertex Pipelines, Feature Store, and Model Registry.

• Fully managed graph-based metadata on Google Cloud
• Native integration with Vertex AI Pipelines
• Enterprise-grade IAM, security, and regional storage

The MLflow Model Registry is a centralized model store, set of APIs, and UI to collaboratively manage the full lifecycle of an MLflow Model. It provides model versioning, stage transitions (Staging, Production, Archived), annotations, and approval workflows — making it the de facto standard for model governance in open-source MLOps stacks and widely adopted across enterprises of all sizes.

• Centralized model versioning and stage management
• Collaborative review and approval workflows
• Framework-agnostic with broad ecosystem integration

ArangoML provides support for common metadata storage across the entire machine learning lifecycle, enabling reproducibility, monitoring, and auditing for your ML models. Built on ArangoDB's native multi-model database engine, it combines graph, document, and key-value capabilities to capture complex ML pipeline relationships — from data lineage graphs to model dependency trees — in a single queryable system.

• Native graph database for ML pipeline lineage
• Multi-model: graph + document + key-value in one engine
• Powerful AQL query language for metadata analysis