Train, evaluate, and deploy machine learning models through a unified no-code interface.
Overview
AutoML is a Domino extension that enables data scientists and domain experts to build, evaluate, and deploy machine learning models through a streamlined, no-code interface.
Powered by AutoGluon, AutoML automates the end-to-end model training pipeline - from data profiling and feature engineering through model selection, hyperparameter tuning, and ensembling - so you can go from raw data to a production-ready model in minutes.
AutoML primarily provides a Model Training workflow. This allows you to configure and run an AutoGluon training job that automatically trains multiple model types, ranks them on a leaderboard, and produces deployment-ready artifacts.
Exported models and notebooks should only be shared with authorized users.
Get started
Prerequisites
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A Domino project with the AutoML extension enabled.
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A dataset in CSV or Parquet format stored in a Domino Dataset.
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Permission to launch Domino Jobs if you plan to train models or run Data Exploration as a Domino Job.
Access AutoML
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Navigate to your Domino project.
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In the left sidebar, scroll down to the Extensions section.
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Click AutoML.
The AutoML landing page displays all existing training jobs. From here you can start a new training job.
AutoML model training
AutoML model training uses AutoGluon to automatically train, tune, and rank multiple machine learning models. The training job wizard guides you through four steps: selecting your data source, choosing a model type, configuring training parameters, and reviewing your settings before launch.
To begin, click New training job from the AutoML landing page.
Step 1: Select a data source
Select a file in the dataset you want to use for training. Once your file is loaded, click Continue to proceed.
You can also click the shortcut in the dataset picker to open the project’s Domino Dataset area in a new browser tab so you can upload files before starting the run.
Step 2: Select a model type
Choose the AutoGluon predictor that best fits your use case. Two model types are currently available:
| Model type | Description | Example use cases |
|---|
| Tabular | For structured data with rows and columns. Supports classification and regression tasks. | Classification and regression, equipment failure prediction, well log analysis. |
| Time Series | For forecasting sequential data where observations are ordered over time. | Production forecasting, demand prediction, anomaly detection. |
| Problem type | Description |
|---|
| Binary Classification | Predict one of two classes (e.g., Failed: 0 or 1). |
| Multiclass Classification | Predict one of multiple classes (e.g., CriticalityLevel: 1, 2, or 3). |
| Regression | Predict a continuous value (e.g., FlowRatePSI). |
The configuration step lets you define your training job’s name, target column, and AutoGluon-specific settings.
Basic configuration
| Setting | Description |
|---|
| Job Name | A descriptive name for this training run (e.g., My training job). |
| Description (optional) | A free-text description to help you identify this job later. |
| Target Column | The column in your dataset that the model should predict. Select it from the dropdown list of available columns. |
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Time Column: The timestamp field used to order observations.
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ID Column (optional): A grouping key for multi-series forecasting.
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Prediction Length: The forecast horizon.
AutoGluon settings
| Setting | Description |
|---|
| Preset | Controls the trade-off between model quality and training speed. |
| Time Limit (seconds) | Maximum wall-clock time for the entire training run. AutoGluon will train as many models as possible within this limit. |
| Evaluation Metric (optional) | The metric used to rank models on the leaderboard, e.g., accuracy for classification and RMSE for regression. |
| Experiment Name (optional) | An optional Domino Experiment name for tracking this run. If left blank, a name is auto-generated. |
Advanced Configuration
Click Advanced Configuration to access fine-grained controls for AutoGluon training.
Step 4: Review and launch
Review all your selected settings on the summary screen. If everything looks correct, click Start Training to launch the job.
You will be taken to the training run’s overview page where you can monitor progress in real time.
Training results
Once a training job is launched, its results page provides comprehensive information about the run’s progress and outputs.
Overview
Model Leaderboard
Diagnostics
Metrics
Outputs
Deployments
The Overview tab displays the training job’s metadata and real-time progress. You can access the Data Exploration view, the Domino Job details page, and Domino Experiment used to track generated models.
The Leaderboard tab ranks all trained models by their validation score. The top-ranked model is the one AutoGluon recommends for deployment.Models are displayed in a table sorted by rank, with metadata related to AutoGluon scoring metrics.Use the All Models dropdown to filter the leaderboard by specific model families.The WeightedEnsemble models combine predictions from multiple base models and typically achieve the highest scores. They may have longer prediction times, so consider this trade-off for latency-sensitive applications.
The Diagnostics tab provides visual insights into model behavior. The available sub-tabs depend on the problem type.Feature Importance: A horizontal bar chart ranking features by their contribution to model predictions. Features with the highest importance have the greatest influence on predictions. Use this chart to understand which variables drive your model’s decisions and to identify potential features for engineering or removal.Model Comparison: Side-by-side comparison of all trained models showing model names with color-coded performance bars, validation scores for each model, and a visual comparison helping you understand how different algorithms performed on your data. The best-performing model is highlighted at the top of the comparison.Classification Metrics (binary and multiclass classification only):This view provides comprehensive classification-specific metrics.| Component | Description |
|---|
| Confusion Matrix | A grid showing predicted vs. actual class labels. Cells are color-coded by frequency, helping identify where the model makes correct predictions and where it confuses classes. |
| Accuracy | Overall proportion of correct predictions. |
| Precision | Weighted average precision across all classes (proportion of positive predictions that were correct). |
| Recall | Weighted average recall across all classes (proportion of actual positives that were correctly identified). |
| F1-Score | Weighted average F1-score (harmonic mean of precision and recall). |
| ROC Curve | For binary classification only. Plots the true positive rate against the false positive rate at various threshold settings. Includes the AUC (Area Under Curve) score. |
| Component | Description |
|---|
| R² (R-Squared) | Coefficient of determination indicating the proportion of variance explained by the model. Values closer to 1.0 indicate better fit. |
| RMSE | Root Mean Square Error: average magnitude of prediction errors, in the same units as the target variable. |
| MAE | Mean Absolute Error: average absolute difference between predicted and actual values. |
| MSE | Mean Square Error: average squared difference between predicted and actual values. |
| Predicted vs Actual Plot | Scatter plot comparing predicted values (Y-axis) against actual values (X-axis). Points close to the diagonal line indicate accurate predictions. |
| Residuals Plot | Scatter plot showing residuals (prediction errors) against predicted values. Ideally, residuals should be randomly distributed around zero with no visible patterns. |
The Metrics tab provides two comparison charts and a summary statistics panel:Validation Scores: A horizontal bar chart comparing every model’s validation score. The best-performing model appears at the top.Training Times: A horizontal bar chart showing how long each model took to train. This helps you evaluate the cost-performance trade-off for each model.Training Statistics: A summary panel displaying key metrics for the overall run.Fit Summary: When available, a raw training summary is shown below the charts for deeper inspection.| Metric | Description |
|---|
| Models Trained | Total number of models trained during the run (e.g., 12). |
| Best Score | The highest validation score achieved across all models (e.g., 0.860). |
| Total Train Time | Total wall-clock time for all model training (e.g., 25s). |
| Avg Train Time | Average training time per model (e.g., 2.1s). |
The Outputs tab provides a Download Notebook to get a Jupyter notebook with the full training configuration, evaluation code, and deployment instructions. This notebook serves as a reproducible record of the experiment and a starting point for further customization.
The Deployments tab groups operational follow-up tasks into three sections:
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Model Registry: Shows whether the model has been registered in Domino and lets you register a model.
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Model API: Shows Model API deployment status and lets you deploy a registered model as a Domino Model API.
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Experiment Tracking: Surfaces Domino Experiment and MLflow Run links for the job.
Data Exploration
Accessible via the dataset link in the Training Results view, the Data Exploration tool lets you analyze data quality, distributions, and correlations, and prepare transformations for subsequent model training.
Data Preview
The Data Preview tab shows a sample of your raw data in table format. You can browse columns and rows to get a quick sense of the dataset’s structure and values.
If your dataset is large or takes a long time to profile, you can re-profile the data with a Domino Job using the Execution dropdown.
Column Analysis
The Column Analysis tab provides detailed profiling information for each column in your dataset.
Correlations
The Correlations tab displays a correlation matrix heatmap for all numeric columns in the dataset. Correlation values range from −1 (strong negative correlation) to +1 (strong positive correlation), with color coding to highlight the strength and direction of each relationship.
Use the Min correlation slider to filter out weak correlations and focus on the strongest relationships. This view is helpful for identifying multicollinearity between features and understanding which variables are most associated with your target column.
The UI also lists highly correlated column pairs below the matrix to help you quickly identify candidates for removal or further review.
Data Quality
The Data Quality tab gives you an at-a-glance assessment of your dataset’s readiness for model training. It includes the following sections:
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Missing Values by Column: A horizontal bar chart showing the number and percentage of missing values for each column that has any. Columns are color-coded by severity: green for less than 5% missing, orange for 5–20%, yellow for 20–50%, and red for more than 50%.
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Missing Value Pattern: A compact visual representation of where missing values occur across columns, helping you identify whether missingness is concentrated or scattered.
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Warnings: Issues that could affect training quality. For example,
Small dataset – consider using best_quality preset for better results.
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Recommendations: Actionable suggestions to improve model performance. These are tagged by priority (high or medium) and category (Target, Preprocessing). Examples include:
Transformations
The Transformations tab lets you define preprocessing steps that will be applied to the dataset before model training. AutoML analyzes your data and suggests recommended transformations based on detected issues.
Recommended transformations
AutoML may recommend transformations for columns with the following issues:
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Identifier columns: Columns where all values are unique and are likely row identifiers (e.g.,
PumpId). Recommended action: drop the column.
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High cardinality: Columns that may be identifiers due to a very large number of unique values (e.g.,
ManufacturerModel). Recommended action: review and potentially drop.
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Missing values: Columns with a significant percentage of null values (e.g.,
OperatingYears at 19.9% missing). Recommended action: fill missing values.
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Extreme outliers: Numeric columns with extreme values detected (e.g.,
FlowRatePSI with 6.9% outliers). Recommended action: clip or remove outliers.
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High missing rate: Columns with a very high percentage of missing data (e.g.,
WellSector at 77% missing). Recommended action: drop the column.
Transformations are included in the exported notebook and can be reviewed and modified in code.
Export a Notebook
At any point during data exploration, you can click the Export Notebook button in the top-right corner to download a Jupyter notebook that contains all of your data profiling results and any transformations you have selected.
This notebook can be opened in a Domino Workspace for further analysis or custom preprocessing.
Deploy a model
After training is complete, you can operationalize the best model from the Deployments tab in the Training Results view.
Register a Model
To register your model in Domino’s Model Registry for versioning, governance, and deployment:
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Open the completed training run.
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Navigate to the Deployments tab.
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In the Model Registry section, click Register Model and fill out the details.
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Click Register to save the model to the registry.
Once registered, the model appears in Domino’s Model Registry and can be promoted into downstream deployment workflows.
Deploy a Model API
After a model is registered:
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In the Model API section of the Deployments tab, click the deploy link.
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Complete the deployment dialog.
Once deployment finishes, the section shows the Model API status and, when available, a direct link back to Domino.
Best practices
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Review data quality for subsequent training. Use the Data Exploration tool to inspect missing values, outliers, and column types. Address high-priority recommendations (especially dropping or imputing columns with more than 30% missing data) before starting a training job.
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Use the dashboard to manage iteration cycles. Search, filter, and switch views on the AutoML landing page as your project accumulates runs. Bulk deletion is useful for clearing experimental jobs that are no longer relevant.
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Choose the right preset. The Medium Quality (Faster) preset is a good starting point for rapid iteration. Once you have identified a promising dataset and target, switch to Best Quality for production models, especially on smaller datasets where the additional training time yields meaningful improvements.
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Set an appropriate time limit. A longer time limit allows AutoGluon to train more models and explore more hyperparameter configurations. For initial exploration, 600–1800 seconds is often sufficient. For production runs, consider 3600 seconds or more.
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Exclude identifier columns. Columns with all unique values (flagged as identifiers) do not contribute meaningful signal and should be dropped before training. AutoML will flag these in both Column Analysis and Recommended Transformations.
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Examine feature importance. After training, review the Feature Importance chart on the Diagnostics tab. If unimportant features dominate, consider removing them and retraining to reduce noise and improve generalization.
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Consider the prediction time trade-off. Ensemble models (e.g., WeightedEnsemble_L2) typically achieve the best validation scores but may have higher inference latency. For real-time applications, compare leaderboard scores with prediction times and consider selecting a simpler model that meets your latency requirements.
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Use the exported notebook for reproducibility. Download the Training Notebook from the Outputs tab to preserve a complete record of your training configuration. This notebook can be re-run in a Domino Workspace and serves as the starting point for any custom modifications.
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Use the Deployments tab for operational follow-through. Register the model after a successful run, then create a Domino Model API from the same page when you are ready to serve predictions.
