AI Education Made Easy
We equip IT professionals and emerging talent with practical, hands-on AI education designed for real-world application—making it easy for organizations to efficiently and affordably upskill teams and onboard interns at scale.
testimonials
Dr. Hasan Jamil, Associate Professor
Computer Science Department, University of Idaho
Ready to teach with Lab.computer?
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Why Lab.computer?
Our IDE solution eliminates the need to create and download complex environments. All you need is a browser
Students can learn practical skills and focus on concepts rather than configuration. Instructors can help students maximize their learning.
Instructors can auto-grade a number of assignments instantly and give feedback individually. Students can get immediate feedback.
LAB.COMPUTER?
Lab.Computer is transforming how organizations train and upskill their tech teams. Our platform delivers hands-on, scalable cutting edge AI education designed for both seasoned IT professionals and emerging talent. Whether you're onboarding interns or advancing your engineering teams, we make it easy to teach, learn, and assess critical technical skills—cost-effectively and at scale.
Are you interested in Lab.computer?
Request a DemoProgram Overview & Modules at a Glance
Module 1: AI Foundations & GPU Acceleration
Duration:1–2 Days
Introduction to AI & ML concepts, types, and industry applicationsGPU vs. CPU: Why GPUs accelerate AI workloadsHands-on with CUDA fundamentals & environment setup
Understand key AI principles and learn how GPUs and CUDA supercharge ML training
Module 2: Computer Vision Essentials
Duration:2–3 Days
Image processing, feature extraction, and CNN basicsObject detection & classification using leading architectures (VGG, ResNet, YOLO)Accelerate CV projects using GPU-enabled libraries (cuDNN)
Build CV pipelines & significantly reduce training time using GPUs
Module 3: NLP & Large Language Models
Duration:2–3 Days
NLP foundations, embeddings, and RNNsTransformers, GPT, BERT, and fine-tuning strategiesHands-on fine-tuning of LLMs using GPUs for business tasks
Train, fine-tune, and deploy LLMs efficiently using GPU acceleration
Module 4: Reinforcement Learning
Duration:2–3 Days
RL fundamentals: MDPs, Q-learning, DQNAdvanced RL: PPO, Actor-Critic methodsScaling RL agents using PyTorch/TensorFlow on GPUs
Implement RL agents & use GPUs to scale experiments for faster iterations
Module 5: MLOps & Deployment
Duration:1–2 Days
Full ML lifecycle, version control, CI/CD, and experiment tracking with CometDeployment strategies: cloud, Docker, KubernetesOptimizing inference pipelines using TensorRT
Deploy and manage AI models efficiently and optimize inference pipelines for scale
Module 6: Capstone Project & Wrap-Up
Duration:1–2 Days
Team-based real-world AI project using GPU accelerationPresentation, peer review & expert feedback
Apply end-to-end AI skills in a project aligned with business needs
Delivery Format
- Live Expert-Led Workshops
- Hands-On GPU Labs
- Assessments & Quizzes
- Team Projects & Presentations
Optional Enhancements
- PyTorch Deep Dive
- CUDA Deep Dive
- Industry-Specific Case Studies
- Post-Training Support & Resources