Operationalize Your AI With Confidence

Estimating rental prices from heterogeneous data (location, property features, regional variations) at scale. Manual approaches lacked consistency and accuracy.

Gradient Boosted Machines applied over millions of property records, with region-specific model variants.

Airflow orchestrated periodic retraining jobs across regional datasets; Docker + Kubernetes deployed models as APIs for consistent and scalable predictions. Prometheus-based monitoring detected regional drift, triggering retraining or rollback as needed. All model and feature variants were centrally registered in MLflow for governance, reproducibility, and lineage tracking.

Delivered accurate rent estimates across 100M+ properties, achieving over 80% accuracy while enabling continuous deployment of updated models with minimal downtime.

Existing parametric models failed to predict throughput accurately in dynamic wireless environments.

Gradient Boosted Trees trained on wireless and WAN features to replace the static model.

Deployed the predictor as a service using Docker + Kubernetes, integrated into the client’s product. Implemented blue-green and canary deployments to safely roll out new model updates. Prometheus monitored real-world prediction accuracy with alerts for drift, while Vertex AI managed retraining runs and experiment lineage. Airflow scheduled periodic evaluations against live traffic logs.

Improved throughput estimation accuracy and replaced a brittle legacy system with a self learning, continuously improving predictor.

Video platforms required new content generated by transferring head poses and expressions from existing clips to create realistic new footage. Manual editing was slow and costly.

Deep learning models for pose transfer, refined with classical computer vision methods.

Orchestrated video frame preprocessing and multi-GPU model training with Airflow. Managed model and code versioning along with test datasets, tracked performance using MLflow, and implemented regression monitoring to detect performance degradation. Monitored for adversarial or unnatural poses and measured GPU cost per frame for efficiency. Canary releases validated new model versions before full deployment.

Delivered realistic generated videos, deployed to production, reducing manual editing overhead and enabling scalable content generation.

Vehicle manufacturers needed an AI tool to evaluate and predict dealer performance and set annual sales targets based on historical and market data.

Developed deep learning regression models to process terabytes of multi-dimensional dealer data for performance prediction.

Deployed on Google Cloud using BigQuery, Dataflow, AutoML, and Vertex AI. Built automated pipelines for data ingestion, preprocessing, model training, validation, deployment, and retraining. Managed data versioning via BigQuery and developed Tableau dashboards with geo maps for interactive dealer performance visualization. Implemented continuous monitoring, performance logging, and alerting to ensure reliability, scalability, and operational efficiency.

Achieved R² > 0.75 by incorporating census, neighborhood, and multiple other datasets. The dealer performance dashboard was delivered to Ford for internal use.

Ad networks required an AI-driven system to accurately estimate and adjust floor prices in real time to maximize revenue and bidding efficiency across diverse regions, demographics, and ad categories.

Utilized Quantile Regression, CatBoost, Q-Learning, and Multi-Armed Bandit algorithms for optimal bid value estimation and adaptive floor price adjustment.

Deployed on AWS with data ingestion through Kafka and model storage on S3. Built automated batch pipelines for daily model training using sampled data from 50B+ ad requests per day, integrating geographical, demographic, time zone, country, and ad-level information. Implemented a live reinforcement learning framework with continuous training and evaluation — leveraging <1% traffic (1 server) for exploration and hourly deployment of updated weights to 500+ production servers 3 for exploitation. Ensured scalability, reliability, and continuous model versioning with automated monitoring and logging.

Achieved a 10% revenue increase by dynamically optimizing floor prices using reinforcement learning and quantile regression models trained on sampled real-time bidding (RTB) data.