| What it does | AutoML that just works. Keep training local.
| Enterprise AI system of record for governing and operationalizing AI at scale
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| Stage | MVP
| Established
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| Founded | Not given
| 2018
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| Based in | Not given
| US
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| Pricing model | Free trial
| Contact for pricing
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| Pricing | Free beta with no billing. Paid plans launching in the future; pricing not yet announced. Join the waitlist for paid plans without payment or purchase commitment.
| Not given
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| Key features | - Python SDK for tuning suggestions
- Local model training with remote suggestions
- Supports classification and regression
- Included CPU demo workloads
- Efficient observation budget allocation
- Model ranking and selection
- Reference scheduler implementations
- HTTPS secure communication
| - Centralized system of record across every model, solution, and agent
- Automated workflows with governance embedded by design
- Full portfolio visibility into cost, tokens, risk and ROI
- Dynamic interoperability across 50+ technologies
- Connective automation for IT, business, and AI owners
- Agentic-powered framework for end-to-end AI lifecycle workflows
- Operational intelligence across ML, GenAI, agentic and vendor AI
- Audit-ready evidence mapped to every regulation and policy
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| What makes it different | - Trains locally, not on the service—your models and data stay on your computer
- Efficient tuning using only half the observation budget of full training
- Outperforms reference schedulers in benchmarks on diverse datasets
- Free beta with no billing or account costs
| - Brings ML, GenAI, and agentic AI to production 10× faster
- System of record that unifies every AI asset in one place
- Reduces time from idea to production to weeks at industrial scale
- Enforces policy and governance at delivery time, not in committee
- Operates above existing systems without replacing them
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | Not checked yet ● no change since last check | 62 ● no change since last check |
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| Trust Flow (Majestic) | 3 | 13 |
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| Citation Flow (Majestic) | 13 | 39 |
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| Referring domains (Majestic) | 2 | 579 |
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hyperopt.dev
ModelOp