AI Model Training: What it is and How it Works

In a market flooded with various AI tools and platforms, knowing how to customize and train an AI model could be the differentiator your business needs to get a leg up on the 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.

Read on for an in-depth look at the process of training an AI model.

What is AI model training?

AI model training is the process of creating a custom, intelligent tool that analyzes and interprets vast amounts of data.

The goal is to have an AI model that can accurately perform certain tasks to reach a clear objective, like:

• Generating new content
• Making predictions
• Classifying information

Data is the most important resource for training an AI model. The data you feed an untrained or pre-trained model should be high-quality and human-curated so it can detect patterns and relationships.

There are different types of AI models and training techniques, but for this article, we’ll focus on generative AI and machine learning.

What is generative AI?

GenAI models use data and human-generated prompts to create new content.

For example, GenAI can help engineers work faster through the design process by using prompts to generate ideas.

What is machine learning?

Machine learning (ML) models use data to make decisions or predictions.

For example, an ML model can analyze past customer data, like purchase trends, to predict other products a customer might enjoy.

Working with existing pre-trained models

Before you start training an AI model, check if there is already an existing pre-trained model that can satisfy your use case. You can apply the model directly or fine-tune it to your specific needs.

Some examples of pre-trained models are:

• BERT (Google): For understanding text, answering questions, and sentiment analysis
• GPT (OpenAI): For text generation, chatbots, and summarization
• T5 (Google): For translation, summarization, and text classification
• DeepSpeech (Mozilla): For automatic speech recognition (ASR)
• CLIP (OpenAI): For understanding images and text together

You can find selections of pre-trained models in repositories like:

• Hugging Face
• TensorFlow Hub
• PyTorch Hub
• Model zoos like Meta, Google, OpenAI, ONNX, etc.

Is it hard to train an AI model?

Training an AI model is easier said than done. Depending on your team’s level of expertise and the complexity of the model’s purpose, you might need some help.

AI tasks like model training are usually left to data scientists or IT workers. These professionals have the technical backgrounds and skills to properly:

• Gather and manage data quality
• Maintain data privacy
• Follow infrastructure requirements
• Understand model functions

With that said, training an AI model with no expertise isn’t impossible. You just need patience and the right resources, such as no-code or low-code AI training tools like Amazon SageMaker, Microsoft AI Builder, Google AutoML, and others.

How to train an AI model in 7 steps

1. Identify the problem

Understanding the problem you need to solve is the first step in training an AI model because it will help you determine the relevant data you need.

Here are a few example use cases:

• Do you need an easier way to identify fraud? The AI model will need data that includes examples of fraudulent activities.
• Are you looking to improve customer experiences? Your AI model needs training on customer habits, demographics, and preferences.
• Do you need a faster way to generate new content? You can use prompt engineering to teach the AI model how to give you the right outputs.

2. Collect, organize, and prepare your data

If you have a history exam tomorrow but you only studied the process of photosynthesis the night before, there’s a good chance you won’t be happy with your result.

Think of training an AI model as a similar scenario. The quality of a model depends on the quality of the data you provide. And in the world of AI, the quality of your data far outweighs the quantity.

Training data should be diverse and free of bias. Using data that is specific to your company helps the model learn the intricacies of your business, which leads to better outputs.

Depending on your resources, you can provide an AI model with real or synthetic data.

• Real data is collected from various activities, like social media interactions and feedback (polls, surveys, reviews, etc.).
• Synthetic data is artificially generated for specific situations. In the healthcare industry, synthetic data is used to train AI models so patient information can stay private.

5 types of AI model training data

Depending on your use case, you’ll need the following types of training data.

• Text data includes information from web pages, books, academic papers, government documents, and other sources. It teaches AI models how to process and generate human language.
• Audio data focuses on music, animal sounds, environmental sounds, and human speech. Models can learn to detect and understand accents and speech patterns.
• Image data includes digital images for tasks like facial recognition and digital medical imaging.
• Video data applies to different video formats and can be used to train applications such as facial recognition or surveillance systems.
• Sensor data includes temperatures, biometrics, or an object’s acceleration. It is used to train AI models for driverless vehicles, industrial automation, and IoT.

The data you use needs to be organized and prepared through data processing. This is a task for data scientists and involves removing inconsistencies and outliers to increase the quality and relevance of your dataset.

3. Choose the right type of AI model

Think back to Step One, where you identified the problem you need AI to solve. Will training a generative AI model or machine learning model help you reach your goal?

Here’s a quick look at the key differences between generative AI and machine learning.

4. Pick a training technique

Next, you need to figure out exactly how to train your AI model. When researching techniques, remember to stay practical by considering:

• Available resources
• Costs
• Computing requirements
• Complexity
• Deadlines

There are tons of training options for generative AI and machine learning and every model training process is different. But we’ll just focus on a few of the most commonly used.

Generative AI training techniques

Transformers
A transformer is a neural network that turns one type of input into a different type of output. Transformers learn the context and meaning of data and track relationships between sequence components.

Transformers are the T in GPT (generative pre-training transformer), which you’ve likely seen with ChatGPT. Almost every large language model (LLM) is powered by transformers due to their ability to translate text and speech in real time.

One popular example of this is Google Translate. You can write a sentence in English, click a button, and then your text is translated into a different language of your choosing.

Generative Adversarial Networks (GANs)
Generative Adversarial Networks consist of two neural networks that compete against each other:

• The generator creates artificial sample data to trick the discriminator into thinking it’s real.
• The discriminator learns to distinguish which samples from the generator are real or fake.

Domain data is fed to the discriminator so it can learn what’s real and what’s fake.

The generator’s job is to trick the discriminator. If the generator is successful, the discriminator needs more training to better detect fakes. If the discriminator is successful, the generator has to change its model to create better fakes.

Diffusion

Diffusion models are primarily used to generate realistic images. Here’s how the process works:

• The diffusion process starts by feeding the model training data, which, in this case, is images.
• Next, random noise (Gaussian noise) is added to the existing data.
• Then, the model reverses the process and transforms the noise into a structured output.

For example, diffusion is like training an artist in painting restoration. A smudged painting may be unrecognizable, but as the artist works to restore it, they are learning the minute details of the original artwork. When finished, they could recreate the painting from scratch.

Machine learning training techniques

Supervised learning

Supervised learning involves training an algorithm with labeled datasets curated by humans. The “supervised” part of this process is the labeled data, which is organized by category or outcome. This gives the algorithm a foundational understanding of the desired outputs.

Image classification is one example of supervised learning. Let’s say you have labeled datasets for different types of plants that include size, coloring, leaf shape, etc. With supervised learning, you can create an application that helps users identify the type of plant in front of them just by taking a picture.

Unsupervised learning

Unsupervised learning doesn’t require labeled datasets or human intervention.

Instead, this technique finds patterns and relationships on its own without understanding the meaning of the data.

An example of unsupervised learning is cross-selling. Think of the recommended products section on an e-commerce site. This section is auto-populated by an unsupervised learning model that scours through customer data, finds patterns, and suggests product add-ons or similar items the customer may enjoy.

Semi-supervised learning

A combination of supervised and unsupervised learning, semi-supervised learning uses labeled and unlabeled data to train models.

In this process, the model is fed a small amount of labeled data and a large amount of unlabeled data. The model is able to understand the labeled data and make adjustments to understand the unlabeled data.

Labeling and organizing data is a time-consuming and expensive process. Semi-supervised learning is a happy medium between the high costs of supervised learning and the complexity of unsupervised learning.

5. Train the model

AI model training is an iterative process. The exact training and validation process depends on the model you’re working with. But in general, you’ll feed your prepared data into a model so it can learn to understand patterns and relationships.

In this training step, you will identify errors and implement changes to increase output accuracy. Feedback helps the system refine itself and adjust its parameters to minimize errors and improve performance.

Beware of overfitting, a common problem when training AI models. This happens when the model becomes biased or starts memorizing a dataset rather than learning from it.

6. Test and validate the model

AI isn’t perfect, so it will likely make mistakes in the early stages of learning.

You can test an AI model’s accuracy by feeding it independent data that wasn’t part of the initial training process.

If it doesn’t perform as expected:

• Fine-tune the model
• Gather more data
• Repeat the training process
• Retest

7. Deploy

When your AI model is accurate and meets expectations, you can deploy it via APIs, in cloud environments, or directly into an application.

The training continues

Once your AI model is trained and deployed, the work continues.

AI is known for hallucinations and errors, so you’ll need to continuously monitor its performance. And as your data increases and evolves, retraining becomes necessary to maintain relevance.

But after all the hard work, experimentation, and training, you’ll have a fully custom AI model that knows your business better than anyone.