No-Code Predictive Maintenance & Quality Control

Machine Learning for Manufacturing

Build predictive models from your manufacturing data without coding expertise. Create tools for equipment failure prediction, quality control, and production optimization using a straightforward, no-code platform.

Explore Platform
Manufacturing Health
Vibration0.0
0%
Temperature65.0°C
43%

use client

Sensor calibration due
Temp. variance exceeds norm
Operational Data
Updated now
Failure Risk
0.0%
Machine StatusOptimal

Manufacturing ML Features

Platform Capabilities

Key functionalities that enable manufacturing teams to build and deploy machine learning models without specialized expertise.

  • Multiple

    Data Types

    Process time-series, categorical, and numerical data.

  • Various

    Model Types

    Classification for defect detection, regression for forecasting.

  • Automated

    Retraining

    Update models with new production data automatically.

  • Configurable

    Alerts

    Set thresholds for maintenance and quality alerts.

Production Optimization Tools

Manufacturing ML Applications

Build specific machine learning models for predictive maintenance, quality control, and production planning.

AutoML

Automated Machine Learning

Upload manufacturing data and build predictive models without coding expertise.

AI Pipelines

Manufacturing Pipelines

Create reusable ML workflows that can be retrained as new production data becomes available.

Model Training

Predictive Maintenance Models

Train models that predict equipment failures and maintenance needs based on sensor data.

Predictions

Production Analysis Interface

Input equipment variables and receive predictions for potential failures or quality issues.

AI Dashboards

Operations Dashboards

Create visualizations of production metrics, equipment status, and prediction results.

Preprocessing

Sensor Data Processing

Automatically clean and normalize time-series data from manufacturing equipment and sensors.

Predictive Maintenance

Machine Learning for Equipment Reliability

Build models that predict when equipment will need maintenance based on sensor data. ML Clever analyzes patterns from historical maintenance records and equipment sensor readings to identify early warning signs of potential failures, helping reduce unplanned downtime.
View Features
Manufacturing Analytics Dashboard

Manufacturing Use Cases

Key Applications

Specific ways manufacturing operations can use machine learning to improve efficiency and reduce costs.

Predictive Maintenance

Equipment Failure Prediction

Build models that analyze sensor data to predict when equipment needs maintenance before failures occur.

Quality Control

Quality Control Prediction

Identify factors that affect product quality and predict which production batches may have issues.

Production Optimization

Production Output Forecasting

Forecast production capacity and resource needs based on historical patterns and current conditions.

Production Planning

Optimize Manufacturing Resources

Forecast production output and resource needs using historical data. Build models that predict how changes to production parameters will affect output quantity and quality, helping manufacturing teams make data-driven decisions about resource allocation and scheduling.
Planning Tools
Production Planning Dashboard

From Data to Predictions

Manufacturing ML Tools

Build, deploy, and use machine learning models specifically designed for manufacturing applications.

Boston Housing
Regression Model
90
Training Complete
Last updated: 2h ago
Model Training

Predictive Maintenance Models

Train models that analyze equipment sensor data to predict failures before they happen, reducing unplanned downtime.

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Make a Prediction

age
sex
chest pain type
resting bp
cholesterol
max heart rate

Prediction Result

0
0: 0%
1: 0%
Predictions

Equipment Analysis Interface

Input current equipment parameters and receive predictions about potential failures or maintenance needs.

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AutoML (advanced) on heart_statlog
random_forest
Score: 95.38
Feature Importance
ST slope
chest pain
max heart
cholesterol
Prediction
0
Result
Target: target
Sample Input Data
age: 54, sex: 1, chest pain: 4...
AI Dashboards

Production Monitoring Dashboards

Create visual displays of production metrics, equipment status, and prediction alerts for operations teams.

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Data Preprocessing
1
Preprocess datasets, configure models, and initiate workflows
Documentation
ML Clever
Model Training
2
Train and evaluate machine learning models with automatic tuning
Documentation
ML Clever
AutoML
3
Streamline training with automated machine learning pipelines
Documentation
ML Clever
AutoML

Automated Model Building

Upload manufacturing data and let the system build the best prediction model for your specific needs.

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Quality Control

Predict Production Quality Issues

Create models that forecast which production batches may have quality issues based on process variables. The platform lets you identify which factors most influence product quality, enabling proactive adjustments to manufacturing parameters before defects occur.
Quality Models
Quality Prediction Dashboard

How It Works

A straightforward process for creating manufacturing prediction models without coding or data science expertise.

  • Data Upload

    Flexible

    Import CSV files with sensor and production data from your manufacturing systems.

  • Model Building

    Automated

    Select what to predict (equipment failures, quality issues) and the system handles the rest.

  • Deployment

    Simple

    Use models through the web interface or connect via API to your existing systems.

Platform Performance

Implementation Metrics

How quickly manufacturing teams can build and deploy machine learning models.

  • Model Building

    No-Code

    Create prediction models without programming.

  • Data Processing

    Automated

    Process sensor and production data automatically.

  • Implementation

    Days

    Deploy manufacturing models in days, not months.

Manufacturing Use Cases

Specific applications of machine learning in manufacturing settings using the MLClever platform.

Equipment Reliability

  • Failure PredictionProactive

    Identify potential equipment failures days or weeks before they occur.

  • Maintenance SchedulingOptimized

    Schedule maintenance when it's needed, not based on fixed intervals.

Production Efficiency

  • Yield OptimizationData-Driven

    Identify optimal production parameters to maximize output quality and quantity.

  • Resource PlanningPredictive

    Forecast material and staffing needs based on production schedules and demand.