AI Model Training & Experiment Tracker UI

Design an AI/ML experiment tracking and model management interface for MLForge, a experiment tracking and model lifecycle management platform.

**MLOps context:**
- Team size: 5-30 ML engineers and data scientists
- ML frameworks: PyTorch, TensorFlow/Keras, Hugging Face Transformers, scikit-learn
- Experiment volume: 50-200 experiments per week across the team
- Deployment targets: AWS SageMaker, Kubernetes (KServe), edge devices (ONNX Runtime)
- Framework: React + TypeScript + Tailwind + Plotly.js

**Design the following modules:**

1. **Experiment Dashboard:**
   - Active experiments: running now with real-time metrics (loss, accuracy, ETA)
   - Recent experiments: table with key columns — name, model, dataset, status, primary metric, duration, user
   - Quick filters: by user, model type, dataset, status (running, completed, failed, stopped)
   - Comparison shortcut: checkbox select experiments then "Compare" button
   - Experiment search: by name, tags, hyperparameters, metric ranges
   - Project organization: folder-like grouping by research initiative or team
   - Pinned experiments: "best so far" markers for easy reference

2. **Experiment Detail View:**
   - Summary header: name, status badge, start time, duration, compute cost estimate
   - **Metrics tab:** real-time charts — training loss, validation loss, accuracy, custom metrics
     - Multi-run overlay: see metrics from multiple runs on the same chart
     - Smooth/raw toggle for noisy metrics
     - Step vs. time vs. epoch x-axis options
     - Auto-scale or fixed y-axis range
   - **Hyperparameters tab:** full config displayed as formatted YAML/JSON with diff highlighting vs. baseline
   - **Artifacts tab:** model checkpoints, output files, generated samples, confusion matrices
   - **System metrics tab:** GPU utilization, memory usage, CPU, disk I/O over time
   - **Logs tab:** stdout/stderr streaming with search, level filtering, and timestamp jumps
   - **Environment tab:** Python version, package versions, hardware specs, Docker image, git commit SHA
   - Reproduce button: generates CLI command or notebook cell to re-run with same config

3. **Experiment Comparison:**
   - Side-by-side table: hyperparameters with differences highlighted (changed values in bold)
   - Overlaid metric charts: training curves from N experiments on one chart with legend
   - Parallel coordinates plot: hyperparameters as axes, experiments as lines, color by performance
   - Scatter plot: any hyperparameter vs. any metric (find correlations)
   - Statistical significance test: are two experiments meaningfully different?
   - "Best experiment" auto-selection based on target metric

4. **Model Registry:**
   - Registered models: name, latest version, stage (staging/production/archived), owner
   - Model versions: each version linked to the experiment that produced it
   - Stage transitions: promote staging to production with approval workflow
   - Model card: description, intended use, limitations, fairness evaluation, performance benchmarks
   - Deployment history: when each version was deployed, by whom, rollback capability
   - A/B test configuration: split traffic between model versions

5. **Dataset Management:**
   - Dataset catalog: name, size, version, creation date, description
   - Version control: track changes between dataset versions, diff statistics
   - Data profiling: column statistics, distribution plots, missing value analysis
   - Lineage tracking: which experiments used which dataset version
   - Annotation management: labeling progress, annotator agreement scores

6. **Pipeline & Deployment:**
   - ML pipeline DAG visualization: data prep then training then evaluation then deployment
   - Pipeline run history: status, duration, output artifacts per stage
   - Deployment dashboard: active models, endpoint health, latency (p50/p95/p99), request volume
   - Canary deployment progress: traffic ramp-up visualization with rollback triggers
   - Cost tracking: compute costs per experiment, per model, per deployment

7. **React + TypeScript + Tailwind + Plotly.js Implementation:**
   - Real-time streaming: training metrics update live via WebSocket/SSE
   - Chart performance: WebGL-accelerated charts for experiments with millions of data points
   - Table virtualization: handle thousands of experiments without pagination lag
   - Dark mode: default for ML practitioners (long screen time)
   - CLI integration: display SDK code snippets for logging metrics, parameters, artifacts
   - Notebook integration: render Jupyter notebook outputs inline in experiment details

ML experiment tracking is how teams avoid repeating work and find what actually matters — make every comparison instant and every insight discoverable.