Tech Term Decoded: Model Training

Definition

In AI, model training is a process that involves teaching a machine learning model to identify patterns and make decisions by feeding data to it. With this training, the model learns to assign inputs input (e.g., images, text, or numbers) to outputs (e.g., labels, predictions, or actions) by adjusting its internal parameters. The objective of model training is to develop a model that can accurately predict outcomes using new data that has never been exposed to it [1].

For example, let’s look at a case scenario of building a real estate recommendation system for property seekers. There is need to use vast and diverse housing data so it can understand property markets broadly, similar to training a new estate agent learning the intricacies of property business.

Just as you'd train a new estate agent by showing them property listings in Lekki, Ikoyi, and Banana Island, teaching them how to assess building quality in flood-prone areas, explaining Lagos land acquisition processes and governor's consent requirements, demonstrating how to verify genuine property documents from fake ones, and understanding rental payment patterns (annual rent upfront), you train an AI model by providing it with real estate data.

The process of model training [2].

Origin

In the formative years of AI, training an AI model was a tedious process as developers used rule-based systems, which required encoding explicit rules for the AI to follow. Though this method had its positives, it couldn't handle the complexity and nuances of real-world data.

AI model training has evolved from rule-based systems to data-driven approaches, with deep learning representing a major breakthrough. Techniques like reinforcement learning, transfer learning, and unsupervised learning have further expanded AI capabilities, enabling models to learn from more complex and dynamic data [3].

Context and Usage

Businesses and organizations depend on model training to customize and train AI models to stay ahead of competition. You can train an AI model to do almost anything, from recognizing patterns to creating new content—as long as you have the right resources. The aim is to have an AI model that can accurately perform certain tasks to achieve objectives such as:

Generating new content
Making predictions
Classifying information
Diagnosing diseases and discovering new treatments
Building AI-Powered Recommendation Systems in Retail
Detecting fraudulent transactions and assessing credit risk.
Development of autonomous vehicles.

Why it Matters

Model training is an important stage in machine learning that leads to a model ready to be validated, tested, and deployed. It determines the success of an AI model. Proper training ensures accurate predictions and the ability to handle diverse scenarios. Inadequate training can result in errors, biases, and inefficiencies, undermining the model’s effectiveness in real-world applications [4].

In Practice

A real-life case study of a company offering model training services can be seen in the case of C3 AI. C3 AI enables distributed training through a mix of out-of-the-box and custom ML pipelines addressing different data science workload demands. The training of these pipelines creates ML models which can be analyzed in the C3 AI ML Studio, promoted for deployment, used for generating score reports, or evaluating model performance. In addition, these models could also be created using no-code drag-and-drop experiences provided by C3 AI Ex Machina [5].

Tech Term Decoded: Model Training

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