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Platform Overview
AutoML experiment sweeps + Airflow-style pipeline orchestration, tracked end-to-end with MLflow
AutoML Engine
scikit-learn · XGBoost · LightGBM

Sweeps 50+ algorithms across Linear, Tree-Based, Ensemble, SVM, KNN and Neural Network families. Every trial is logged to MLflow — metrics, params, and serialised model artefacts. Use the AutoML page to rank algorithms on any dataset in one click.

50+ Algorithms MLflow Tracking Auto-Ranking Model Registry
Pipeline Orchestration
Airflow-inspired DAG engine

Runs ML workflows as Directed Acyclic Graphs — the same paradigm used by Apache Airflow. Three built-in pipelines: Training (ingest → train → register → deploy), Retraining (drift detection → retrain → A/B test → promote), and Data Processing (clean → encode → scale → feature store).

DAG Execution Drift Detection Auto-Retraining Staging Deploy
Load Dataset
AutoML Sweep
MLflow Tracking
Pipeline DAG
Model Registry
Deploy
Live Experiment Metrics
Total Runs
{{ total_runs }}
all time
Completed
{{ completed_runs }}
finished successfully
Best Score
{{ best_metric }}
accuracy / R²
Experiments
{{ n_experiments }}
active datasets
Algorithm Categories
Runs by Dataset
Recent Training Runs — each row is one MLflow-tracked experiment
View all
{% for r in recent_runs %} {% else %} {% endfor %}
Run IDAlgorithmCategoryDataset Primary MetricDurationStatus
{{ r.run_id }} {{ r.algorithm }} {{ r.category }} {{ r.dataset }} {{ r.primary_metric }} {{ r.duration }}s {% if r.status == 'FINISHED' %} Done {% elif r.status == 'RUNNING' %} Running {% else %} {{ r.status }} {% endif %}
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No runs yet
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