In recent years, Artificial Intelligence has made its way into the industry, enabling efficiency, automation, and smarter decisions. However, many companies that have tried to apply "generic" Machine Learning models in their plants have encountered a surprise: they don’t work as expected.

Why does a model that predicts with great accuracy in a lab or another factory fail when applied to your process?

The answer is simple: every industrial plant has its own data DNA.

The Problem with Generic Models

Most Machine Learning models are trained on a specific dataset. If that model learns patterns based on data from a chemical company, its knowledge is shaped by the variables, sensors, environmental conditions, and operating routines of that plant.

When that same model is applied in a different setting—say, a food or pharmaceutical plant—the correlations change:

• The sensors have different calibration or sampling frequency.

• The raw materials have natural variability.

• Operators follow different protocols.

• And the process conditions (temperature, pressure, pH, timing) are not equivalent.

As a result, the model starts to “guess” instead of “predict.” Its outcomes become unreliable, and decisions based on them can even create more errors than benefits.

The Role of Domain Adaptation and Transfer Learning

This is where two key concepts in the industrial field come into play: domain adaptation and transfer learning.

• Domain adaptation or parameter tuning means adapting an existing model to a new data environment, adjusting its parameters to learn the differences between both contexts.

• Transfer learning involves “transferring” part of the knowledge from a trained model (for example, the layers that learn general relationships) and retraining the final layers with data specific to the new process.

In practice, this allows you to leverage the foundation of a generic model (e.g., a polymer quality prediction model) and tailor it to your actual line (e.g., your specific mix of raw materials and operating conditions).

The result is a more efficient and accurate model that understands the local context without starting from scratch.

Best Practices for Building Truly Useful Models

When developing or adapting machine learning models for specific industrial processes, three key principles should be followed:

1. Collect local and representative data

   Importing an external dataset is not enough. Models must be trained on data that reflects the reality of each plant: sensors, environmental conditions, operators, shifts, materials, and actual variations.

   
   

2. Retrain regularly

   Industrial processes evolve: pumps, sensors, raw materials, or procedures change. If the model isn’t updated, it will start to degrade. A regular retraining plan (monthly, quarterly, or when conditions change) keeps its accuracy long-term.

   
   

3. Validate with process engineers

   Industrial AI can’t be a black box. Validating predictions with engineers’ expertise helps detect inconsistencies, adjust thresholds, and build trust in the model.

Contextualized ML: The Path to Real Efficiency

Machine Learning can’t replace operational knowledge—but it can amplify it.

The models that truly generate value in industry are not the largest or most sophisticated, but the ones that understand the context of each process:

• Which variables really matter.

• How the system behaves under normal conditions.

• When a deviation is a real issue and not just noise in the data.

That’s why the future of industrial ML lies in context-trained models, which learn from the environment they operate in, evolve with it, and are explainable to the people making decisions on the plant floor.

In Summary

A generic model can be a good starting point, but never the destination.

Every production line has its own history, its own data language, and its own operational particularities.

Training models that understand that language not only improves accuracy—it also gives control back to the teams who truly understand the process.