NomadicML provides a Visual AI platform for understanding, validating, and training Physical AI systems using video data. The platform offers tools for lightning-fast video search, specialized agent models for analysis, and a Python SDK for automation. It supports use cases across driving, robotics, and infrastructure monitoring by transforming video footage into actionable intelligence.
Funding
Funding not disclosed
Founders
Product
Problem
Machine learning (ML) systems often suffer from performance degradation after deployment due to factors like data drift and evolving user behavior. Identifying and correcting these issues requires continuous monitoring and optimization, which can be complex and time-consuming. Existing solutions often lack the ability to adapt to new data patterns and user preferences in real-time.
Solution
NomadicML provides an enterprise-grade platform for the continuous optimization of ML systems, ensuring models maintain peak performance from pre- to post-production. The platform enables teams to define custom evaluation metrics, systematically optimize model parameters, and continuously tune AI systems in response to new production data. By automating hyperparameter tuning and providing real-time performance insights, NomadicML helps close the performance gap in ML systems, ensuring accuracy, efficiency, and security. The platform supports a range of applications, including retrieval-augmented generation (RAG), LLM safety, and transcription/summarization tasks.
Target Audience
NomadicML is designed for ML engineers, ML researchers, and applied data scientists deploying complex AI systems across various industries, from solo developers to enterprise teams.
Features
- Centralized platform for managing and streamlining ML experimentation workflows
- Automated hyperparameter optimization using state-of-the-art parameter search techniques
- Support for custom evaluation metrics, including LLM-as-a-judge integrations
- Real-time performance monitoring and continuous tuning capabilities
- Rapid experimentation to boost AI performance in the face of new production data
- Integration with both open and closed-source LLMs, leveraging favorable pricing for tokens and compute costs
- Customizable statistical summaries, visualizations, and score distributions for justifying post-production choices