Disarray offers an ML engineering platform that captures, structures, and makes searchable an organization’s machine‑learning best practices and artifacts. It automates repetitive tasks such as data discovery, pipeline generation, and iterative experimentation while integrating with existing warehouses, feature stores, experiment trackers, and orchestration tools, giving developers transparent, audit‑able control over domain‑specific decisions. The result is faster model development without disrupting established workflows.
Funding
Funding not disclosed
Founders
Product
Problem
Machine learning teams often lose productivity due to undocumented institutional knowledge, repetitive manual tasks, and difficulty integrating new automation tools into existing data and ML infrastructure. These gaps create errors, blind spots, and slow the translation of ideas into production models.
Solution
Disarray provides a platform that captures and structures an organization’s ML best practices, techniques, and contextual decisions, making them searchable and reusable. It automates repetitive, structured work such as data discovery, pipeline construction, and iterative experimentation while preserving developer control over domain‑specific, ethical, and contextual judgments. The system integrates with existing warehouses, feature stores, experiment trackers, orchestration frameworks, and monitoring tools, allowing teams to adopt new capabilities without disrupting established workflows. By delivering transparent, inspectable automation, Disarray enables engineers to focus on model objectives and quality judgments rather than rebuilding known solutions.
Target Audience
Primary customers are enterprise data science and machine learning engineering teams that need to accelerate model development while maintaining control and compliance within their existing infrastructure.
Features
- Centralized knowledge base that ingests and organizes public and private ML artifacts for easy reuse
- Automated data discovery and pipeline generation that respects existing feature stores and data warehouses
- Seamless integration with common experiment tracking, orchestration, and monitoring platforms via native connectors and APIs
- Contextual guidance engine that surfaces relevant best‑practice recommendations during model development
- Full auditability and traceability of automated steps, allowing developers to intervene and inspect at any point
- Extensible SDK for custom workflow extensions and organization‑specific conventions