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AI Toolkit & Sandbox
A modular cloud and edge environment to design, train, fine-tune and validate AI models — with a secure sandbox for experimentation and seamless push-to-marketplace publishing.
notebook.ipynbrunning
from tasmu.sandbox import AutoML, Dataset
ds = Dataset("qa-traffic-cams-2026")
auto = AutoML(task="object-detection", budget="2h")
model = auto.fit(ds.split("train"))
model.evaluate(ds.split("val"))
# mAP@0.5 = 0.83 · latency p95 = 24 ms
model.publish("marketplace", visibility="sovereign")Capabilities
Low-code / No-code IDE
Drag-and-drop model design for business analysts and citizen developers.
AutoML
Automated model selection, hyperparameter tuning and evaluation.
Version & experiment tracking
Full lineage across runs, datasets, metrics and artifacts.
Synthetic data
Generate representative datasets for training and “what-if” analyses.
Explainability & bias
Responsible AI checks embedded into every experiment.
Multi-user collaboration
Shared workspaces with fine-grained access controls.