Cloud Workstations

Build systems around highly available workflows, SLA guaranteed.

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One-click production deployment

Any data developer can develop and test production-grade data and ML workflows locally and deploy them in production with a single click - no changes in the code required. Unify data and ML, AI workflows on a unified platform, following the best practices developed at Netflix and other modern data-driven organizations.

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Hardening flows for production

Through the adoption of Outerbounds, we've had an inside look at a wide range of behind-the-scenes AI projects. To give you a sense of the variety, consider these examples:

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1. Everyone starts at Stage I: You create a quick demo or a POC with off-the-shelf APIs with a few rounds of ad-hoc prompt engineering. Eventually it becomes evident that the system needs more domain-specific data to produce more accurate, domain-specific, and timely results.
2. In Stage II, you introduce RAG or another mechanism to incorporate domain-specific data into the system. The more high-quality data the system can access, the better the results. This typically gets you to a local maximum in performance, which may or may not be sufficient for production use. Considerations like cost, data privacy, and long-term stability may prompt you to explore further refinements.
3. In Stage III, you begin exploring more advanced models to address the limitations. While it's easy to experiment with various off-the-shelf APIs, the focus may shift toward hosting models internally or fine-tuning open-weight models to address the needs of your specific domain.

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Integrate to other flows and systems

Flows are not islands. They can react to events and data from surrounding systems and other flows. The platform comes with a highly available event-bus that allows you compose sophisticated, reactive data and machine learning systems.

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