IoT + ML

Smart sensors meeting intelligent predictions

Our Process

Step-by-Step Workflow

Every project follows this proven 10-step process to ensure quality, accuracy and on-time delivery.

Step 1

System Architecture Planning

Design the complete IoT+ML pipeline — sensors → data → model → action.

Step 2

IoT Hardware Setup

Configure microcontrollers and sensors to collect real-world data continuously.

Step 3

Data Collection Pipeline

Build automated data logging to CSV / cloud database from IoT sensors.

Step 4

Data Preprocessing for ML

Clean IoT time-series data, handle noise and missing values for training.

Step 5

ML Model Training

Train model on IoT-collected data for anomaly detection, prediction, or classification.

Step 6

Edge / Cloud Deployment

Deploy model at edge (on device) or cloud for real-time inference.

Step 7

Real-time Prediction Integration

Feed live sensor data to trained model and trigger alerts or actions.

Step 8

Dashboard Development

Build visual dashboard showing sensor values and ML prediction results live.

Step 9

Documentation

Full report covering hardware, data pipeline, model and results.

Step 10

Final Demo & Delivery

Live working demo of complete IoT+ML system with all source files.

🔗

Get in touch and our team will reach out within 2 hours with a customised plan and quote.