Vendor Name | Spell |
Stand-alone vs. Platform | Standalone platform with community, self-serve, and managed deployment options. |
Delivery Model | Primarily managed cloud service, with custom delivery as needed for enterprise |
Clouds Supported | Available on public cloud (AWS, GCP, Azure) and full enterprise-level on-prem deployment |
Pricing Model | Seat-based license pricing or custom |
Service Level Guarantees | Depends on your plan. |
Support | Every company in subscription or contract is provided a private slack channel for direct engineering support 24/7. |
SSO, ACL | All users are provided user access management within collaboration features. |
Security and Compliance | User access management features |
Setup | Fully automated, CLI-based deployment process that connects user cloud profile and compute access to platform from pip installation and one command. |
Flexibility, Speed, and Accessibility | Combination of pre-defined metadata and user-defined custom metadata. |
Model Versioning, Lineage, and Packaging | Supports end-to-end model lineage. |
Log and Display of Metadata | Durning experimentation and training, Spell tracks and displays live: hardware metrics (e.g., Disk, GPU), built-in framework-specific metrics (e.g. Keras val and loss), user-defined metrics (from Python API), execution information (e.g., run start/stop), compute machine state, user-specified hyperparameters, source control information (e.g., git hash, last commit message), stdout/err logs, and others. |
Comparing Experiments and Models | Provides both tabular and visual metrics inspection and comparison tools. |
Organizing and Searching Experiments and Models | Experiments are organized by project directories and further grouped into subsets of individual runs. |
Model Review, Collaboration, and Sharing | Supports review and collaboration across training runs, model registry, and deployed models, through shared interface for organization. |
CI/CD/CT Compatibility | Tight integration with git and leverages Github Actions for CI/CD. |
Integrations | Deep integrations with Arize, Grafana, WandB, Github/Github Actions, Tensorboard, as well as premade AMIs with common ML libraries installed (PyTorch, Tensorflow, etc.). |
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Vendor |
Demo link |
Stand-alone vs. Platform |
Delivery Model |
Clouds Supported |
Pricing Model |
Service Level Guarantees |
Support |
SSO, ACL |
Security and Compliance |
Setup |
Flexibility, Speed, and Accessibility |
Model Versioning, Lineage, and Packaging |
Log and Display of Metadata |
Comparing Experiments and Models |
Organizing and Searching Experiments and Models |
Model Review, Collaboration, and Sharing |
CI/CD/CT Compatibility |
Integrations |