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• Led development & model training of YOLOv5 for real-time object detection, achieving 93.2% accuracy on a 200,000 image dataset, enhancing system reliability in object identification and classification. 
• Mitigated class imbalance through GAN-based data synthesis, generating varied night/day image sets, which boosted model generalization & detection accuracy by 25% in challenging lighting conditions 
• Implemented the edge device deployment of AI application through optimized Docker containers and Flask APIs, reduced setup time by 60% and enabling seamless deployment across diverse systems 
• Applied model quantization using Intel OpenVINO, achieving an 80% improvement in real-time inference speed, critical for resource-constrained environments 
• Designed and implemented Ansible Playbooks to setup infrastructure and multi-container deployment, eliminating manual work. 
• Implemented MQTT protocol using Mosquitto to build secure and reliable message broker system and transfer data safely over 100 edge devices.

Guise AI at the edge leverages local compute to extract meaningful data delivering better insights for enterprises. We built our technology from the ground up using our patent pending Continuous Learning AI which powers the most adaptive, accurate and efficient streaming data models in the industry.

Our technology deployed at the edge delivers feedback rapidly and locally within the system. Localized processing is more efficient (low latency) and it increases the level of security in terms of data privacy. Clients and Partners who require an accurate, cost-efficient, and secure solution turn to Guise AI to extract patterns and predict from real-time and dynamically changing data in creating the greatest impact for end-users.

Guise.AI focuses on developing innovative AI technologies and positively contribute to the economy, society, and humanity.

• Attained 98% detection accuracy for multi-class object detection models focused on precise recognition of vehicles, enhancing the Auto-Focus (AF) system of the Sony Alpha 7R V Camera
• Delivered over 94% accuracy in key-point detection model for recognizing critical object attributes in real time, improving the camera’s sharp focus in wildlife photography
• Accelerated model training by 50% using distributed training with AWS Horovod, boosting efficiency and streamlining large-scale model development cycles

BAAM.ECO

Sony Corporation is a multinational consumer electronics brand that designs and develops electronic equipment for consumer and professional markets. Their products range includes categories like cameras, mobile, tablet, smart devices, Televisions, home theater, audio products for home, Car &amp; marine and also gaming consoles. Sony is also into music & motion pictures production and financial services. Sony products retail out of their offline stores and through authorized online retailers. In 2020, the company recorded annual revenues of $79.9 B and a net profit of $10.1 B.In 2021, the company recorded annual revenues of $89.9B and a net profit of $7.99B

Analyzed 10+ academic papers and evaluated object/key-point detection models, implementing top models to improve player identification accuracy by 30%
• Developed custom pose classification model using PyTorch, with 98% accuracy in precise player movement analysis
• Created an automated annotation system for baseball videos, reducing manual labeling time by 70%
• Designed a high-throughput video processing pipeline using Django, SQL, Celery, and RabbitMQ, enabling the concurrent processing of 1K videos/hr, improving system efficiency by 80%

Vishwanathan Niranjan

M Raghuraman

V Suresh Shankar

Vijaykumar Ivaturi

Vijaya Kumar

Manvi Jairath

Prarthana Jairath

Suresh V Shankar

DriveAnalytics provides an online machine learning-enabled platform to rapidly build products and solutions in the areas of computer vision and Natural Language Processing technologies.

AI-based analytics product development solutions

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