Research & Technology
Model Evaluation Framework
A comprehensive model evaluation platform that goes beyond aggregate accuracy. Decomposes model performance into fairness, robustness, calibration, and distributional shift components with statistical confidence intervals. Used by ML teams for production readiness assessment and by compliance teams for responsible-AI reporting.
Distributed Training Library
A training library derived from peer-reviewed research on gradient compression, communication-efficient optimization, and heterogeneous cluster scheduling. Each algorithm ships with a published methodology paper, benchmarked results with proper ablation studies, and real-time performance monitoring against theoretical baselines. Supports PyTorch and JAX.
ML Experimentation Platform
A hosted research environment with curated datasets, statistical testing frameworks, and reproducibility tooling. Designed for ML researchers who want to move from hypothesis to validated result without spending weeks on infrastructure. Includes built-in multiple-comparison correction and proper cross-validation procedures.
UTexas Academy
Professional training programs in ML systems design, deep learning theory, and production ML engineering. Courses are taught by active researchers and practitioners, with curricula grounded in the latest academic literature. Available as live cohort-based programs, self-paced online courses, and custom on-site workshops.
Model Validation & Audit
Independent validation of ML models, training pipelines, and evaluation methodology. Our review process mirrors academic peer review: two independent reviewers assess methodology, statistical validity, and implementation correctness. Deliverables include a detailed validation report suitable for regulators, investors, and board-level stakeholders.
Research Publications & Datasets
A growing library of working papers, benchmark datasets, and reference implementations published under open-access licenses. Topics span efficient training, model compression, evaluation methodology, and ML systems architecture. We believe open research accelerates progress for the entire industry.