Our Labs

Standard Python development environment for data exploration, visualization, and introductory ML. Pre-configured with Python 3.11, NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn.

Single-GPU environment for training deep learning models, running computer vision pipelines, and experimenting with neural network architectures. Pre-loaded with PyTorch, TensorFlow, and CUDA toolkit.

High-performance multi-GPU environment for distributed training, large model experiments, and advanced deep learning research. Supports data parallelism, model parallelism, and mixed-precision training.

High-memory GPU environment purpose-built for fine-tuning large language models. Supports full fine-tuning of 7B+ parameter models and parameter-efficient methods (LoRA, QLoRA) for larger models up to 70B.

Environment for deploying, serving, and benchmarking LLM inference. Students learn to optimize serving throughput, configure quantized model deployment, and build production API endpoints.

Pre-configured environment for building retrieval-augmented generation systems. Includes vector databases, embedding model APIs, document processing pipelines, and evaluation frameworks.

Full-stack data engineering environment on GCP. Students build batch and streaming data pipelines, work with data lakes, and create feature stores for ML systems.

Production ML infrastructure environment. Students build CI/CD pipelines for ML, deploy models to Kubernetes, set up monitoring, and implement A/B testing and canary deployments.

GPU-accelerated environment for image and video processing. Pre-loaded with large-scale vision datasets, annotation tools, and state-of-the-art model implementations.

High-performance simulation environment for robotics AI. Students work with physics simulators, train RL agents, and develop perception and navigation systems in realistic virtual environments.

Environment for optimizing and deploying AI models on edge hardware. Includes model compression tools, TensorRT optimization, and remote access to physical NVIDIA Jetson and Coral Edge TPU boards at our Houston lab.

Isolated network environment for AI-powered cybersecurity training. Students build intrusion detection systems, analyze malware, and conduct red/blue team exercises in a safe sandbox.

HIPAA-aligned environment for healthcare AI development. Pre-loaded with de-identified medical imaging datasets (X-ray, CT, MRI), clinical NLP tools, and FHIR-compliant test servers.

Full-featured VS Code IDE in your browser — with integrated terminal, file tree, git, extensions, and everything needed for real software engineering work. Pre-loaded with Claude Code CLI, Node.js, Python 3.11, Docker, and Git.