ZELLA: Efficient & Robust Model Training
Transforming Ideas Into Deployed AI Solutions
Unlock the Unpredictable World
Building high-quality AI models requires efficient, robust, and scalable training methodologies—especially when handling long-tail data, noisy environments, and large-scale datasets. ZELLA by AvaWatz is engineered to optimize AI model training, reducing computational costs while maximizing accuracy and generalization. Leveraging cutting-edge techniques in efficient learning, robust optimization, and continual training, ZELLA enables faster AI development while ensuring models perform effectively in dynamic, real-world conditions.
Advanced Training Features
BALANCED TRAINING FOR LONG-TAIL DISTRIBUTIONS.
ROBUST HANDLING OF NOISY DATA.
COMPUTE-EFFICIENT FRAMEWORKS FOR FASTER TURNAROUND.
SCALABILITY FOR MULTI-MODAL DATA.
FINE-TUNING FOUNDATION MODELS FOR SPECIFIC USE CASES
MODEL ADAPTION, CONTINUOUS LEARNING
Case Study 1
Robust AI Training for Powerline Inspection
Problem
AI-based drone inspections struggled with rare defect detection due to imbalanced datasets.
Solution
ZELLA improved model generalization by augmenting rare defect samples and refining training strategies.
Results
Model Performance: Achieved high accuracy with Mean Average Precision over 80% for FOD detection and classification.
- 3x improvement in defect detection accuracy
- Lower false positives, reducing unnecessary inspections
- More reliable automated monitoring with fewer failures
Case Study 2
Improving Debris Detection for Imbalanced Datasets
Problem
Airport and road debris detection AI models missed small hazardous debris due to dominant common classes in training data.
Solution
ZELLA balanced the dataset, enhancing AI's ability to detect rare, high-risk debris.
Results
Model Performance: Achieved high accuracy with Mean Average Precision over 80% for FOD detection and classification.
- 50% reduction in false negatives for hazardous debris
- AI adapted to diverse weather conditions
- Lower operational costs through more accurate AI predictions
Case Study 3
Medical Imaging AI for Outlier & OOD Data Filtering
Problem
AI-assisted tumor detection models struggled with out-of-distribution (OOD) cases, leading to higher false negatives.
Solution
ZELLA's OOD detection system flagged uncertain cases, allowing human review and improved model refinement.
Results
Model Performance: Achieved high accuracy with Mean Average Precision over 80% for FOD detection and classification.
- 30% reduction in misdiagnoses due to OOD data
- Better generalization across diverse patient datasets
- Higher confidence in AI-driven medical imaging
