Stage 3: Supervised Training – Teaching the Model with Labeled Data
With a labeled dataset in hand, we can move from exploration to teaching. Stage 3 is where AI stops just noticing patterns and starts learning to predict outcomes. This is the essence of supervised training: feeding the model examples of inputs along with their correct labels until it can generalize and classify new, unseen data.
What Supervised Training Means
In supervised training, the AI model learns a mapping between input features (sensor readings, video frames, audio clips) and output labels (normal vs fault, pedestrian vs forklift, generator on vs off). Each labeled example is a teacher, guiding the model toward the right associations.
- Input: vibration frequency spectrum
- Label: “bearing fault”
- Input: site video frame
- Label: “worker with hard hat”
- Input: sound profile
- Label: “generator start”
After thousands or millions of such examples, the model develops the ability to recognize these outcomes in real time.
Training Pipeline
The supervised training process typically includes:
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Dataset Preparation
From Stage 2, labeled data is organized into training, validation, and test sets. This ensures the model can be evaluated properly. -
Model Selection
Choosing the right architecture (convolutional neural networks for vision, recurrent or transformer models for audio, etc.) depending on the task. -
Training Iterations
The model adjusts its internal weights through optimization (like gradient descent), minimizing the gap between its predictions and the true labels. -
Validation & Testing
Periodically, the model is tested on unseen labeled data to check for overfitting and measure performance metrics such as accuracy, recall, precision, or F1 score. -
Checkpointing
Promising versions of the model are saved for further optimization and eventual deployment.
Example: Construction Site Monitoring
On a Digital View AI board project, suppose Stage 2 produced a labeled dataset of jobsite video:
- Cluster A: labeled as “pedestrian traffic”
- Cluster B: labeled as “forklift operation”
- Cluster C: labeled as “idle area”
Supervised training now produces a model that can classify new video frames into these categories automatically, the first step toward real-time safety or workflow monitoring.
The Powers of Prediction
Supervised training is where the AI’s predictive power is created. The quality of the labeled dataset directly determines how effective the model will be in the field. Without Stage 3, the labeled data is just static information. With it, the data becomes a dynamic inference engine.
For Digital View boards, this stage usually happens on more powerful infrastructure, GPUs in the cloud or on-premises servers. Once trained, the model will be prepared for the next step: optimization and deployment at the edge.
Transition to Stage 4
Supervised training gives us a strong, working model, but not one ready to run efficiently on the Pi CM5 and M.2 accelerators. That’s the job of Stage 4: Model Optimization, slimming down the model so it’s fast, efficient, and deployable on Digital View boards.
