What is AI Model Training and How Does It Work?

2. Choose an AI model

Let’s see our checkmark list – you have a problem, you’ve identified what type of data you need to solve it, know what data sources you can use, and what technique would work best for you. The next step would be to select the right type of AI model. Which one will help you reach your goal?

3. Choose a training method

Next, you have to figure out how you will train your AI model. Every model training process is different. Since we focus on generative AI and machine learning, let’s break down a few of the most commonly used training options for these specific models.

Generative AI training methods

The most prominent training techniques for generative AI models include:

• Generative Adversarial Networks (GANs): Two networks, the generator and discriminator, are trained in an adversarial process. The generator creates synthetic data that is indistinguishable from real data, and the discriminator acts as a critic and determines whether the data it receives from the generator is real or fake. The generator continuously tries to fool the discriminator, while the discriminator gets better at detecting deepfakes.

• Variational Autoencoders (VAEs): Learns to encode data into a compressed latent representation and then decode it back into its original form. By sampling from the learned latent space, VAEs can generate new data that is similar to the training data but with variations.

• Transformer-based models: The core of the transformer is the self-attention mechanism. It allows the model to weigh the importance of different parts of the input data during processing. Many transformers also have an encoder to process the input and a decoder to generate the output. With vast amounts of data, these models learn to predict the next element in a sequence.

Machine learning training methods

ML training techniques are typically categorized by the nature of the learning process and the data used. The three primary categories include:

• Supervised learning: The model is trained on a labeled dataset (each piece of training data has a known output or “ground truth”) to learn the mapping function that best approximates the relationship between the input and output variables. Specific supervised learning methods include linear and logistic regression, decision trees, and random forests.

• Unsupervised learning: The model is trained on an unlabeled dataset to find hidden patterns, structures, or relationships within the data without any predefined outputs. Specific unsupervised learning methods include K-Means and Hierarchical Clustering, PCA, etc.

• Reinforcement learning: It focuses on how an "agent" should make "choices" in an "environment" to gain the most overall "reward." The agent learns by seeing the results of its actions instead of being directly taught. Specific reinforcement learning methods include Q-Learning, Deep Q-Networks, policy gradient methods, etc.

4. Train the model

Feed your AI model with the prepared data to help it learn patterns and relationships, while continuously identifying and correcting errors to improve accuracy. In this process, use the feedback to refine the model and adjust its parameters for better performance.

A key challenge you have to keep in mind during the training process is overfitting – when the model memorizes the training data instead of actually learning from it – leading to poor performance on new, unseen data.

5. Validate the model

After training, you must validate the AI model using separate, more complex datasets to check for overfitting and ensure it can generalize beyond the training data. The validation can help you reveal gaps or weaknesses in the model’s training. In some advanced setups, techniques like Retrieval-Augmented Generation (RAG) can further enhance model performance by injecting relevant, external information during inference.

6. Test the model

Lastly, you have to test the AI model on independent, real-world data to assess its readiness for deployment. If it performs well, it can go live; if not, you might need to collect further data, retrain, and fine-tune your model.

Remember that even after deployment, you still have to perform ongoing monitoring to catch errors, adapt the model to new data, and refine the overall performance.

How to scrape data using Web Scraper API

One of the most convenient ways to get real-world data from the web is to use a scraping provider like Oxylabs. And since we’re gathering data for training or testing an AI model, let’s look at how to use the Web Scraper API to gather real-time data from Google Search, as an example.

1. Prepare your environment

We’ll be using Python to access the Web Scraper API. If you don’t have it in your system already, you can download the latest version of Python from the official website.

After that, create a new Python file named main.py in any folder you prefer, open up a terminal window, and run this command:

pip install requests

This will install the necessary libraries used in Python for accessing APIs. We’ll be using requests for the API call and the json module to store our retrieved data in JSON format.

2. Scrape Google Search data

Now that our environment is ready, we can proceed with scraping Google Search data. First of all, let’s import the necessary libraries at the top of our Python file.

import json
import requests

After that, we can define the payload of our request. This is where we define the search query, the Google domain to use, and the number of pages to return, along with other parameters. Feel free to adjust the parameters for your own needs.

Make sure the parse parameter is defined as True, so that the API returns a structured, preparsed object with the results. You can find the full documentation for scraping Google search results here. It should look something like this:

payload = {
    "source": "google_search",
    "domain": "com",
    "query": "adidas",
    "start_page": 1,
    "pages": 1,
    "parse": True,
}

Next, let’s send the request to the Web Scraper API like this:

response = requests.post(
    "https://realtime.oxylabs.io/v1/queries",
    auth=("USERNAME", "PASSWORD"),
    json=payload,
)
response.raise_for_status()

Make sure to replace the USERNAME and PASSWORD fields with your own Oxylabs Web Scraper API credentials. You can find them in your Oxylabs dashboard.

Now that the request is complete, we can prepare the data for storage. Let’s extract the organic search results from Google, which refer to search results that don’t include ads and sponsored links. It should be enough to add this line:

data = response.json()["results"][0]["content"]["results"]["organic"]

3. Store data in a JSON file

Once we have our data ready, let’s use the json library to store the data in a JSON file. It should be as simple as this:

with open('google_data.json', 'w') as f:
    json.dump(data, f, indent=2)

That’s it! Try running this command to scrape publicly available Google Search data through the Web Scraper API:

python main.py

If you don’t see any error messages, check for a google_data.json file in the same folder as your Python file is located.

4. Complete Google Search code example

Here’s the complete code for using the Oxylabs Web Scraper API:

import json
import requests

payload = {
    "source": "google_search",
    "domain": "com",
    "query": "adidas",
    "start_page": 1,
    "pages": 1,
    "parse": True,
}

response = requests.post(
    "https://realtime.oxylabs.io/v1/queries",
    auth=("USERNAME", "PASSWORD"),
    json=payload,
)
response.raise_for_status()

data = response.json()["results"][0]["content"]["results"]["organic"]

with open('google_data.json', 'w') as f:
    json.dump(data, f, indent=2)

Final thoughts

To successfully train your AI model, you must provide it with high-quality, well-structured information and to regularly evaluate its performance. One of the common practices in AI training is to adjust the model based on real feedback – whether from users or developers. A well-trained AI model can become not only an effective tool but also a reliable partner in solving complex problems.

So, now that you understand how to train or fine-tune your LLM model, the next step is gathering the right data. This can be done easily using web scraping tools. For example, Oxylabs offers a Web Scraper API – an all-in-one web data collection platform that supports every stage of the scraping process. If you’d like to dive deeper into acquiring high-quality web data for LLM fine-tuning, check out our in-depth "Acquiring High-Quality Web Data for LLM Fine-Tuning" whitepaper.