Amazon Lightsail
The fastest path from AI concept to production with predictable cloud infrastructure.
The open-source standard for machine learning model versioning, metadata tracking, and reproducibility.
ModelDB is a pioneering open-source system designed to manage machine learning models, their pipeline metadata, and associated artifacts. Originally developed at MIT and now maintained by Verta.ai, ModelDB serves as the foundational infrastructure for MLOps, focusing on the critical need for reproducibility in data science. The system architecture utilizes a centralized database to log all aspects of a machine learning experiment, including hyperparameters, code versions, training data, and performance metrics. In the 2026 landscape, ModelDB distinguishes itself by offering a vendor-neutral, highly extensible framework that allows engineering teams to maintain full sovereignty over their model metadata without being locked into proprietary cloud ecosystems. Its core technical value lies in its structured schema that enables complex querying across thousands of experiments, facilitating advanced insights into model drift and feature importance over time. It supports a wide array of environments, from local development to large-scale distributed training clusters, ensuring that every model iteration is documented, auditable, and deployable with high confidence.
Native support for Python, R, and Scala, allowing heterogeneous teams to log experiments to a single repository.
The fastest path from AI concept to production with predictable cloud infrastructure.
The open-source multi-modal data labeling platform for high-performance AI training and RLHF.
Scalable, Kubernetes-native Hyperparameter Tuning and Neural Architecture Search for production-grade ML.
The enterprise-grade MLOps platform for automating the deployment, management, and scaling of machine learning models.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses a relational schema to allow SQL-like queries across experiment parameters and results.
Maintains a directed acyclic graph (DAG) of how data, code, and hyperparameters produced a specific model.
Abstracted storage layer supporting S3, Azure Blob Storage, GCS, and NFS.
Enforces a strict logical organization of work to prevent metadata fragmentation.
Automatically captures Git SHAs and environment specifications for every run.
WebSocket-driven dashboard for monitoring training progress across multiple nodes.
Managing thousands of Fine-tuning runs makes it impossible to identify the optimal config manually.
Registry Updated:2/7/2026
Download the exact weights associated with the winning run
Regulators require proof of how a credit scoring model was generated 2 years ago.
Multiple data scientists are testing different feature sets on the same dataset, causing confusion.