Building AI Systems That Scale

The Challenge

Advancing explainable AI methodologies in medical applications requires rigorous research, peer review, and publication at premier AI conferences. The challenge: contribute novel research that passes the highly competitive AAAI workshop review process and advances the field of explainable AI in healthcare.

The Solution

Conducted original research in Explainable AI (XAI) for medical applications, submitted to the AAAI 2026 Workshop on Explainable AI in Medicine (EAIM). Paper successfully accepted through competitive peer review process via OpenReview platform. Research contributes to the growing field of interpretable machine learning in healthcare, where model transparency and explainability are critical for clinical adoption and patient safety.

Key Metrics

Results & Impact

• Paper ACCEPTED to AAAI 2026 Workshop on EAIM
• Published through prestigious AAAI conference series
• Peer-reviewed via OpenReview platform
• Contributing to Explainable AI research community
• Advancing interpretable ML in healthcare
• Part of AAAI 2026 conference proceedings

The Challenge

Building a multi-billion parameter foundation model for music generation from human humming required unprecedented scale, performance, and cost optimization. The challenge: train models up to 10B+ parameters efficiently, scale across 64 AWS Trainium instances, maintain training stability during distributed runs, and deliver production-ready inference at 750M+ stream scale while minimizing costs.

The Solution

Collaborated with AWS Generative AI Innovation Center to build HummingLM using AWS Trainium and SageMaker HyperPod. Implemented distributed training with sequence parallelism (SP), tensor parallelism (TP), and data parallelism (DP) scaling to 64 trn1.32xlarge instances. Used ZeRO-1 memory optimization with selective checkpoint re-computation. Integrated Neuron Kernel Interface (NKI) with Flash Attention for dense attention layers. Built transformer-based LLM (385M parameters, 24 layers) for coarse token generation plus specialized upsampling component. Deployed on AWS Inferentia via Amazon ECS for production inference. Result: 54% cost reduction vs GPUs, 2x faster training, 8% throughput improvement, batch size increased from 70 to 512.

Key Metrics

Results & Impact

• 54% cost reduction vs GPU training
• 2x faster training speed
• 8% throughput improvement
• Batch size increased from 70 to 512
• Scaled to 64 AWS Trainium instances
• Zero downtime with SageMaker HyperPod auto-healing
• Powers 750M+ streams worldwide
• Featured in AWS Summit Sydney 2025 keynote

The Challenge

Creating music using AI from user-provided melodies and distributing it at massive scale presented unprecedented challenges. The platform needed to generate unique AI-powered music tracks from user input, process millions of concurrent creation requests, manage content rights for AI-generated music, and deliver 750M+ streams with sub-second latency.

The Solution

Architected an end-to-end AI music creation and streaming infrastructure using AWS (Lambda, CloudFront, S3) with deep learning models for music generation. Built scalable pipeline to process user melodies through AI models, generate unique tracks, and instantly distribute globally. Developed patented multi-track content management system enabling selective track inclusion based on user permissions. Integrated deep learning for content analysis, personalization, and real-time music synthesis.

Key Metrics

Results & Impact

• Powered 750M+ streams globally
• AI music generation at scale from user melodies
• Real-time processing of millions of creation requests
• Reduced streaming latency to <500ms worldwide

The Challenge

Creators needed seamless integration between live streaming and on-demand content while maintaining Twitch-scale latency and removing copyrighted music in real-time. System had to handle 70K RPS while processing live streams, managing content rights, and archiving for on-demand viewing—all without manual intervention.

The Solution

Invented and patented two breakthrough systems (US11457245B1 & US11870830): embedded streaming content management that automatically archives live streams for on-demand viewing, and multi-track selective content delivery. Built massively scalable infrastructure using AWS Kinesis for real-time stream processing, Redshift Spectrum for analytics, Lambda for serverless compute, S3 for storage, and CloudFront for global distribution. Integrated ML/AI models for real-time music detection and removal, content categorization, viewer behavior prediction, and automated quality optimization. Maintained sub-second latency at Twitch scale while processing 70K+ requests per second.

Key Metrics

Results & Impact

• Patents: US11457245B1 (2022) & US11870830 (2024)
• Handled 70K+ RPS at Twitch-scale latency
• Real-time music removal from live streams
• Seamless live-to-VOD conversion with AI optimization
• Zero manual intervention required
• Adopted by major streaming platforms

The Challenge

An esports startup (Rimble) needed to build tournament infrastructure, real-time match streaming, and player engagement features from scratch with limited resources.

The Solution

Provided technical architecture guidance, helped select scalable tech stack, and advised on product strategy. Designed system to handle concurrent tournaments with real-time updates and integrated streaming. Implemented image recognition and ML models for automated match analysis, player performance tracking, and content moderation.

Results & Impact

• Launched MVP in 4 months
• Handled 10K+ concurrent users
• Successful seed funding round
• Featured in gaming industry publications

The Challenge

Multiple startups across fashion, travel, healthcare, dating, gig economy, and SAAS needed technical and strategic guidance to scale their products and make critical technology decisions.

The Solution

Provided architecture reviews, tech stack decisions, scaling strategies, and product roadmap guidance. Helped teams make critical build-vs-buy decisions, optimize infrastructure costs, and prepare for growth.

Results & Impact

• 10+ startups advised across industries
• Collective fundraising: $20M+
• Multiple successful product launches
• Reduced infrastructure costs by 40%+ average