NO-CODE AUTOML FOR MANUFACTURING
AutoML: A virtual data scientist for manufacturing decisions.
Prepare data, explore features, validate models and explain results in one workflow.
AutoML automates data preparation, feature engineering, model training and evaluation while process experts define the target and validate results against real manufacturing conditions.
CORE CAPABILITIES
From data readiness to model quality, in one workflow.
AutoML handles repetitive modeling work while process, quality and equipment teams define the use case, target and validation criteria.
Data Quality Diagnostics
Check formats, completeness, time range and anomalies before modeling so MES, equipment and quality data can be used with confidence.
Automated Feature Engineering
Transform time, lot, equipment and process fields into features that reflect actual process conditions and remain interpretable to engineers.
Automated Model Generation
Test multiple algorithms and parameter combinations, then compare candidates against the target metric and validation data.
Model Performance Insights
Review validation results, key drivers and applicability range to support prediction, optimization and deployment decisions.
BUILT FOR MANUFACTURING
Start with the manufacturing problem, not the algorithm.
Manufacturing teams need models that connect back to process conditions, quality signals and engineering decisions.
Start with Manufacturing KPIs
Frame the project around quality, yield, equipment or process metrics that the operation already tracks.
Process Experts Stay Involved
A no-code workflow lets process, quality and equipment teams participate in modeling, validation and improvement.
Understand the Key Drivers
Connect model results to process variables and feature importance so engineers can investigate what is changing.
Bring Models into Engineering Workflows
Use models for virtual metrology, parameter optimization, quality prediction and anomaly detection within existing engineering workflows.
MANUFACTURING USE CASES
Begin with one measurable manufacturing problem.
AutoML is more than automated model selection. It helps turn structured manufacturing data and process context into models that can be validated, reused and applied in operations.
Virtual Metrology
Predict quality measurements that are costly or slow to obtain using historical process and equipment data, so engineers can identify drift earlier.
Quality and Yield Prediction
Use process conditions, equipment signals and material-lot data to identify important drivers and support process adjustment and root-cause analysis.
Anomaly Detection and Equipment Maintenance
Combine normal operating patterns with equipment signals, alarms and process conditions to identify abnormal behavior and support inspection or maintenance planning.
Process Parameter Optimization
Explore multivariable relationships and parameter combinations to identify a more stable operating window across quality, throughput and material loss.
AUTOML WORKFLOW
From manufacturing data to models that support engineering decisions.
Data preparation, feature engineering, model generation, evaluation and explanation stay in one workflow, with manufacturing teams defining the target and judging whether the result is usable.
Data Preparation
Combine MES data, equipment signals, quality measurements and process conditions, then define the prediction target and evaluation metric.
2Feature Engineering
Transform time, lot, equipment and process fields into features that capture meaningful process variation.
3Automated Modeling
Compare multiple algorithms and parameter combinations to identify models suited to the target and process conditions.
4Decision and Deployment
Review drivers, validation results and application limits before prediction, optimization or deployment.
Define the prediction target, evaluation criteria and data scope before modeling. AutoML then automates repetitive modeling work while the manufacturing team validates the result.
Transform time, lot, equipment and process fields into features that capture meaningful manufacturing variation.
Test algorithms and parameter combinations, then compare model quality against the target metric and validation data.
Review key drivers, validation results and application limits before prediction, optimization or deployment.
MODEL EXPLAINABILITY
See what drives a prediction, how the model was validated, and where it applies.
Switch between prediction drivers, model comparison and follow-up analysis to understand how a model was built, validated and should be used.
Understand the Drivers Behind Each Prediction.
Engineers need more than a number. They need to see which variables matter, how the model was validated and where the model is applicable.
- 01Target and DataDefine the prediction target, data range and evaluation criteria.
- 02Model EvidenceReview key drivers, model explanation and validation results.
- 03Application LimitsConfirm the operating range and where human review is required.
Model output supports engineering decisions; it does not replace process expertise. High-risk use cases should retain human review and version control.
Do Not Stop at the Top-Ranked Model.
Compare candidate models together with evaluation metrics, validation conditions and important features to understand the trade-offs that matter in production.
- 01Candidate ModelsCompare algorithms and candidate versions.
- 02Model QualityReview error, validation data and feature importance.
- 03Engineering Trade-OffChoose a version based on performance, speed and operational risk.
Model ranking does not replace process validation. Confirm the data range and actual operating conditions before use.
Keep Improving the Model After the First Build.
Review data preparation, feature settings and important variables to identify opportunities to improve model performance or engineering understanding.
- 01Improvement DirectionReview the impact of data preparation and feature settings.
- 02Prediction EvidenceUnderstand how important features influence model output.
- 03Application DecisionUse the analysis to decide whether to predict, adjust or deploy.
Follow-up analysis supports model improvement and understanding. Actual use still depends on data conditions and process expertise.
MANUFACTURING OUTCOME
Connect model performance to a manufacturing KPI.
A useful case should make the data conditions, model output and operational result clear.
Manufacturing case study
Improve Parameter Settings with Equipment and Process Data.
Profet AI manufacturing applications include parameter optimization, virtual metrology and anomaly detection. Each project starts with a defined manufacturing KPI and validates the data, model and result step by step.
Read the manufacturing case →DOMAIN TWIN™
Turn individual model results into reusable manufacturing knowledge.
AutoML is a core capability within Domain Twin™, Profet AI’s enterprise AI brain for manufacturing. Domain Twin™ connects frontline know-how, data, models and AI applications so validated knowledge can be governed, reused and scaled as AI assets across lines and plants.
AutoML
Turn structured manufacturing data into explainable predictive models for quality, equipment and process improvement.
AI Studio
Turn enterprise knowledge and models into AI assistants, digital employees and workflow-connected applications with governed access.
AILM
Track models, processes and improvement records so results remain traceable, reusable and governable over time.
Start with one AutoML use case and build toward Domain Twin™. Explore Domain Twin™ →
FAQ
Three questions to answer before you start.
Clear data conditions, a measurable manufacturing target and process-owner involvement matter more than choosing an algorithm first.
What manufacturing problems are a good fit for AutoML?
Problems with structured historical data and a measurable target, such as virtual metrology, quality or yield prediction, anomaly detection, equipment maintenance and process parameter optimization.
What do we need before starting?
Identify the data sources, field definitions, time range, prediction target and how the result will be evaluated on the shop floor. AutoML can diagnose data quality, but business definitions and data ownership still require the manufacturing team.
How can a model be used after it is built?
Depending on the use case, the model can support prediction, parameter optimization, follow-up analysis or deployment. The output should always be interpreted within the data scope and process context used for validation.
A GOOD STARTING POINT
What does a practical first project look like?
You do not need a large data-science team. Start with one manufacturing problem that has a clear data source, a measurable target and an accountable process owner.
Structured Data Is Available
For example MES records, equipment signals, quality measurements, alarms, sensor data or structured historical records.
The Target Is Measurable
For example lower error, higher yield, less material loss, earlier anomaly detection or shorter measurement lead time.
A Process Owner Is Involved
Process, quality, equipment and IT/OT teams can jointly validate whether the model is useful and ready for workflow adoption.
START WITH A REAL MANUFACTURING PROBLEM
Bring one manufacturing use case. We’ll help assess whether AutoML is the right fit.
Tell us the prediction, quality, equipment or process issue you want to improve. We’ll review the data conditions and recommend a suitable demo setup.
Use the form to request an AutoML demo or consultation.
Tell us about your manufacturing use case.
Share the problem you want to improve. Our team will follow up on the right demo and data requirements.