AI Workbench

Bitwisen AI Workbench is a hands-on environment for learning, testing, and understanding artificial intelligence methods without setting up a Python environment or cloud notebook. Explore six connected studios: • Supervised Learning — regression and classification • Unsupervised Learning — clustering and anomaly detection • Semi-Supervised Learning — scarce-label classification • Reinforcement Learning — planning and control in interactive environments • Deep Learning Studio — neural-network architectures and training mechanics • LLM Studio — tokenization, attention, local next-token generation, retrieval, and modern language-model workflows The Workbench includes 70 classical and tabular algorithms, 20 bundled datasets, 10 reinforcement-learning environments, a transparent teaching transformer, tested examples, and run-specific Model Mechanics. Change parameters, train models, inspect metrics and visualizations, make predictions or replay policies, and save project or result files. Designed for transparent learning: • Numerical work runs locally in the application for the bundled models and examples. • Model Mechanics explains the equations, settings, intermediate values, and limitations behind a run. • Built-in examples make it easy to compare methods and see how choices affect results. • Display preferences such as accent color, text size, spacing, contrast, and motion are saved automatically between launches. • LLM Studio can optionally download browser-compatible pretrained model files when the user explicitly requests them; the bundled teaching model does not require that download. Bitwisen AI Workbench is intended for education, transparent experimentation, and small-to-medium learning problems. It is not a substitute for production AI infrastructure, regulated analysis, clinical decision support, safety-critical control, or professional review. Suggested category Education Developer / support email [email protected] Suggested website https://ai-engineer.org/workbench.html Recommended privacy-policy URL after deployment https://bitwisen.com/aiworkbench/privacy.html