Get Started with the Ramblr Data Engine

Cloud-based tools to manage, organize, and analyze large-scale video datasets.

• Data Management: Store data, manage annotation versioning, and organize datasets in project and workspace structures.

• Dataset Curation: Create domain-specific subsets or “playlists” for targeted model training.

• Dataset Insights: Identify objects of interest and extract key dataset insights using the Ramblr Data Engine.

• Custom Vocabulary Management: Define and maintain project-specific or workspace-wide vocabularies.

• Synchronized Visualization: Align and review video data with associated sensor inputs for detailed analysis, with the ability to compare two videos side by side.

Optimize video labeling with AI automation, structured workflows, and scalable team management.

• Workspaces and Workflow Management: Organize teams, projects, and pipelines while assigning tasks and coordinating labeling workflows.

• AI-Driven Labeling: Automate data processing and annotation to reduce manual effort and improve efficiency.

• Scalable Workforce Management: Assign roles, set permissions, and manage in-house or outsourced teams.

• Comprehensive Oversight: Use analytics dashboards, version control, and cloud-based tools for full visibility.

• Efficient Scaling: Distribute workloads and manage large datasets with cloud-based storage and processing.

Stay in control of your labeling projects with Ramblr’s analytics dashboard, designed to track progress, ensure quality, and optimize workflows.

• Project Progress Monitoring: Track annotation completion rates, task assignments, and team efficiency.

• Quality Assurance Metrics: Measure labeling accuracy against ground-truth, detect inconsistencies, and integrate automated or human-in-the-loop review processes.

• Performance Analytics: Analyze workforce productivity, optimize task distribution, and identify bottlenecks to improve efficiency.

Automate labeling while maintaining high-quality standards through human oversight.

• High Labeling Accuracy: Implement automated and human review processes to maintain precision.

• Customizable Quality Control: Adjust review frequency and feedback loops using pipeline templates.

• Human-in-the-Loop Workflows: Integrate manual verification into labeling pipelines for consistent and controlled annotations.

The Ramblr Data Engine automates dataset creation and optimizes training workflows, making AI model development more efficient and scalable.

• Curate Datasets: Create domain-specific “playlists” from your data for targeted model training.

• Data Splitting: Easily divide datasets into training, test, and evaluation sets.

• Aligned Multimodal Data Sources: Integrate video, audio, text, and sensor data with spatiotemporal and contextual annotations to improve accuracy.

• Scalable Model Training: Export training data in standardized formats (e.g., RLE mask encoding) and integrate with existing training frameworks.

Ensure continuous improvement with performance monitoring and validation.

• Performance Monitoring: Track model accuracy, detect drift, and refine datasets to maintain quality.

• Validation and Testing: Use labeled test datasets to evaluate model outputs and ensure robustness.

• Continuous Improvement: Update models with newly generated and enhanced datasets for ongoing optimization.

Ramblr’s active learning framework measures accuracy and improves AI model accuracy with continuous, targeted human feedback. 

• Object Detector: Use zero-shot detection as a starting point to train a powerful, custom object detector on your data and vocabulary. 

• Event and Activity Models: Continuously improve and adapt event detection and activity prediction models to new scenarios with human guidance.

• Bring Your Own Model: Integrate and improve your trained model within Ramblr’s pipelines.

Export annotations and metadata seamlessly, integrating high-quality labeled data into your AI training workflows.

• Version-Controlled Exports: Ensure consistency with dataset versioning for full reproducibility and traceability.

• Custom Export Formats: Export annotations and metadata in COCO and JSON.

• Cloud and Local Storage: Securely export data to cloud repositories, local storage, or third-party platforms.