Twelve Labs, a pioneering video AI startup, is setting new benchmarks in the industry with its development of Marengo-2.6, a multimodal AI model that understands video content with human-like comprehension. With a $50 million Series A funding round co-led by Nvidia, Twelve Labs aims to redefine video search functionalities through its Video Understanding Platform.
Twelve Labs relies on both high-quality annotated data and a robust managed infrastructure to build models capable of indexing and searching petabytes of video with precision. By using SuperAnnotate for data creation and Databricks Mosaic AI for model development, they ensure both the delivery of optimal datasets and efficient model fine-tuning, accelerating their AI development process.
The challenge
Developing a sophisticated AI model like Marengo-2.6 requires top-notch data, particularly when handling video content. The sheer volume and variety of video data and complex processes meant that creating proprietary datasets is essential.
Twelve Labs initially collaborated with a well-known annotation company, hoping for a smooth data annotation process. However, they soon encountered multiple roadblocks: inconsistent quality, communication inefficiencies, and delays jeopardizing their project timelines.
The solution
SuperAnnotate emerged as the perfect partner for Twelve Labs, offering a platform and service for rapid, accurate and scalable multimodal data creation and management.
Key elements of this successful collaboration include:
- Purpose-built platform: SuperAnnotate's ability to support custom workflows streamlines the management of complex video annotation tasks, from writing captions and verifying search queries to refining model-generated descriptions.
- Quality control tools: SuperAnnotate provides diverse quality control features, including simple thumbs-up and thumbs-down tools, that allow Twelve Labs to maintain high evaluation standards.
- Flexible UI: The visual interface for video review enhances user experience, ensuring consistently high quality.
- Responsive communication: SuperAnnotate is distinguished by its exceptional communication. Twelve Labs found the team to be highly responsive, quickly implemented feedback, and continuously refined the platform to serve Twelve Labs' needs better.
- Dataflow from SuperAnnotate to Databricks Mosaic AI: High-quality annotated data from SuperAnnotate is directly fed into Mosaic AI's fine-tuning pipeline, allowing seamless integration.
Results
The partnership with SuperAnnotate has delivered transformative results for Twelve Labs:
- Accelerated time-to-market: By reducing project completion times by 50%, SuperAnnotate has enabled Twelve Labs to bring innovations to market faster, providing a critical competitive advantage in the fast-paced AI industry.
- High-quality annotations: SuperAnnotate consistently delivers top-tier annotation quality that aligns with Twelve Labs’ rigorous standards.
- More frequent model iteration: Faster data delivery timelines meant Twelve Labs could run model training cycles up to 2x faster than before.
- Enhanced team productivity: The user-friendly tools and responsive support offered by SuperAnnotate have streamlined the annotation process, freeing up Twelve Labs' team to concentrate on core research & development.
Conclusion
SuperAnnotate has become an indispensable partner for Twelve Labs, streamlining its multimodal data creation process. SuperAnnotate’s ability to provide high-quality, customized datasets ensured that Twelve Labs could reduce project timelines by 50%, while the seamless integration of this data into Databricks Mosaic AI’s fine-tuning pipeline allowed for more efficient model development.
The combination of ease of use, speed, and exceptional responsiveness provided by SuperAnnotate has optimized Twelve Labs' workflow, allowing their team to focus on what they do best: innovating in the video AI space. The result? A faster time-to-market for new features and products gives Twelve Labs a crucial edge in a competitive industry.
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