Model Training

Machine learning models don’t come with pre-programmed knowledge. It takes sufficient amounts of input data to train a model, but once the process is complete, ML can process large volumes of production data, search for patterns in it, and make fairly accurate predictions.

Those results are entirely reliant on a robust ML model training program. If a business can pin down model training, it can enjoy the increased revenue and reduced costs associated with high-performance machine learning models.

For example, manufacturing facilities might use computer vision AI to detect anomalies and defects in their finished products. Proper training boosts the accuracy of those models and reduces the chance of a defective unit entering the market.

What Does Machine Learning Model Training Involve?

When a model receives training in a supervised setting, it receives sample data and attempts to find a relationship between the data and the target label. For unsupervised learning, training often involves finding a pattern in the input data itself. In both instances, AI trainers tweak the weights until the model makes the predictions they expect.

For a typical training session, practitioners select sets of data and a training algorithm. There are three datasets for training, validation, and testing purposes, respectively. AI engineers must cleanse these sets before feeding them to the model, as poor quality data negatively impacts the predictions.

The choice of algorithm depends on the use case of the model, as algorithms can vary in complexity, performance, and other aspects.

How Does Model Fitting in Machine Learning Work?

Model fitting is an iterative process where developers feed an algorithm a training dataset and guide it into producing the predictions they expect. The model slowly starts associating specific features of the input with the intended output.

This fitting process can occur as supervised or unsupervised learning. A supervised learning session involves both an input dataset and some intended output values. Depending on how the model’s prediction varies from the intended output, developers train the model to make more accurate predictions each time.

In an unsupervised session, the model slowly picks up on patterns in the input data. The clusters it creates make up the learning portion of the session, as no reference output is ever necessary.

How To Create a Machine Learning Algorithm Through Model Training

A robust ML model takes contributions from various experts in the company, from project planners to data scientists to software engineers. The complete procedure, model training included, follows.

Keep Your Eye on the Ball

Define the problem you’re trying to solve with machine learning. The answer here isn’t always clear or well-defined, and pinning down the objective helps you design appropriate input datasets and their intended outputs.

This step may require collaboration with subject matter experts if the model will operate in a specific domain. For instance, you’ll need specialized knowledge to know how production data will differ potentially from the training data. You’ll also learn the metrics with which you can measure the model’s performance out in the field.

Gather the Datasets

The next step is to gather the training datasets and pre-process them. Sift through the data and resolve any duplicates, erroneous entries, and inconsistencies. Quality data is at the heart of accurate predictions.

You’ll want two sets of data at this stage: one for training the machine learning model and one for evaluating it. Having these two sets be distinct will ensure unbiased testing later.

It’s also a best practice to start with smaller datasets first, as you can catch errors before you start feeding in larger input data.

Choose a Model Type

As previously mentioned, your choice of algorithm depends on what you want to achieve. Examples of models include:

  • Linear regression
  • K-means
  • SVM
  • Random forest
  • Bayesian
  • Neural networks

These options all differ in terms of cost, computational power, infrastructure needs, and other factors. For the sake of testing, it’s worth choosing a simple algorithm to act as a benchmark when we evaluate model performance later.

Begin Training

Start feeding training sets into the ML model. Based on the output, tune the model’s parameters to improve accuracy iteratively.

Model trainers have access to two types of parameters. Hyperparameters determine the architecture of the model itself, and customizing them fundamentally changes how the algorithm searches for correlations between its input and output. Parameter tuning is the practice of identifying and tuning hyperparameters.

There’s also the trainable parameter, the parameters that the model itself works with as it undergoes training. These parameters control how the model generates an output based on its input dataset.

You might have heard a term called the “loss function,” which measures how “close” the model’s prediction is to the actual intended output. If the deviation is significant, the trainable parameters must change dramatically to improve accuracy.

Companies tend to work with multiple versions of the algorithm at a time. For each, they perform parameter tuning, train the model, and evaluate performance. Whichever models result in the best predictions will expose the best combination of parameters for the job.

Undergo Post-Launch Efforts

Model training doesn’t end here. Developers must also test against a validation dataset, one that differs from the training dataset. Testing with an unseen example is the best way to check how resilient your chosen parameters are and how likely they will succeed in a real-life scenario.

While the business can be confident that the model works well with current training and validation datasets at this point, it can never guarantee the same level of performance if the input data ever changes in the real world. This issue, known as model drift, is the reason why ongoing maintenance will still be mandatory.

Continue to check up on model performance well after deployment. More testing and potential retraining should always be on the table.