Dashboards, charts, model management, data, training, and inference components for ML platforms. Grouped by category.
Dashboard showing model accuracy, F1, loss and accuracy-over-epochs chart.
GPU/CPU/Memory utilization cards with progress bars and cluster summary.
Dual-axis line chart for training loss and accuracy over steps.
Heatmap grid for classification confusion matrix with labels.
ROC curve line chart with FPR/TPR axes and optional AUC label.
Card showing model metadata: name, version, framework, size, last updated.
Table of model versions with metrics, status, and deploy actions.
Card for dataset metadata: name, size, train/val/test splits.
Bar chart showing class/label distribution in a dataset.
Card showing run ID, dataset, duration, hyperparams (epochs, batch size, lr).
Table of experiment runs with run ID, status, metrics, and view link.
Input field for prediction and result display (e.g. class labels and scores).
Progress and counts for batch inference (total, done, failed).
Side-by-side prompt (user) and response (model) for LLM/NLP UIs.
Card showing prompt/completion/total tokens and estimated cost.
Image upload/preview area with detections list (labels and confidence).
Before/after metric comparison with delta (e.g. accuracy, F1, loss).
Row of status pills (running, failed, success, pending).
Run metrics summary with Export and Share actions.
Timeline of pipeline steps with completed/running/pending status.
List of services with health status and optional latency.