What is AI Model Training? Everything You Need to Know

AI isn’t conscious. AI uses a far simpler blend of algorithms that can’t “think” or “feel.” This simplified process is called a “model.” Thanks to new training methods, models are smarter, more efficient, and increasingly woven into our daily lives.

If you’re curious about training your own AI, read on to get a high level understanding of the process.

What is AI Training (and Why Should You Care)?

We teach AI models through a training process. Humans first learn to eat, walk and talk. LLMs first learn the basics such as math, reading and sentence structure. As you move through school, you’ll learn daily skills like math and reading. Then, you’ll learn other skills you’ll never need. AI follows a similar process as well. Once they can process input and generate output, models are trained using larger datasets than you or I could ever imagine.

Thanks to newer methods, these datasets are shrinking. Smaller datasets yield smaller models. Better data begets leaner and meaner AI. Google and Microsoft are now shipping laptops with built-in AI. As computing improves, models become more efficient. Soon, AI will run natively on smartphone hardware. By 2050, you might have deep philosophical conversations with your toaster.

AI Training In the Real World

AI models are already being used in many places you’d never think to look. By now, we’re all familiar with chatbots and image generators. Real-world applications of AI and machine learning branch out much farther than you might think.

• Healthcare: Models are increasingly trained on medical data. They’re often used to speed up diagnoses and to detect rare health conditions that doctors rarely encounter.
• Pharmaceuticals: Models create hypothetical compounds and analyze their efficacy. These pseudo-trials can save years–even decades–of trial and error compared to traditional methods.
• Finance: In the late 2010s, people realized just how efficient AI models are at analyzing trading patterns. Nowadays, AI-driven trading is an industry standard for both crypto and stocks.
• Entertainment: Netflix, Spotify, and even YouTube use trained models to recommend new content for you. These models likely analyze your media consumption to accurately predict what you’ll enjoy next. Remember when Netflix recommendations were garbage? Their improvement correlates directly with the rise of AI.
• Aerospace: NASA uses AI models to analyze planetary data. This allows for better study of Earth and distant planets as well.

The list above is just the tip of the iceberg. Zero-shot learning now allows AI make decisions with data it’s never seen. As new training methods emerge, high-quality models are steadily integrating into your everyday life. Imagine an oven that knows how to cook your food to perfection–we’re not far off from that!

AI Model Training Process

When we teach a child to read, we don’t hand her an encyclopedia and walk away. First, we teach her letters. Then we move on to words and eventually sentences. From sentences, we move to paragraphs, then full books. These same incremental steps apply to AI models. First, a model learns to process input (read data). Then, it learns to generate output. After enough training, a model can begin learning on its own. Once we’ve fine-tuned it, we test and deploy the model for real-world use.

Step 1: Preparing the Data

Models need data. Before we even choose a model, we first need to decide what data to train it on. Data should be clean, well-formatted, and reflective of real-world patterns.

Raw data is often noisy, inconsistent, and incomplete. Before feeding it into your model, you need to clean and format your data. Whether your model trains on structured data like spreadsheets or completely unstructured data like text and videos, quality and relevance are key. You wouldn’t train a self-cooking oven to play golf!

High-quality data shortens training time and yields a small but smart model. Our dataset marketplace offers clean, ready-to-use data out of the box.

Step 2: Selecting a Training Model

You need to select the proper training model for the AI you wish to create. You might use one of the models below–or a combination of them.

• Large Language Models: Often used for chatbots. They’re trained on vast datasets and designed to process human language naturally. LLMs will read and generate text by making predictions based on their training data. ChatGPT, Claude, and DeepSeek are all examples of LLMs.
• Convolutional Neural Networks: These models are used to analyze images and videos. Real-world examples include ResNet, EfficientNet, and YOLO (You Only Look Once).
• Recurrent Neural Networks and Transformers: These models excel at prediction, speech recognition and sequential data. GPT and BERT are widely used examples of this. LLMs are actually an offshoot of transformers.
• Decision Trees and Random Forests: Decision Trees and Random Forests are ideal for data classification and predictive modeling. This type is best for financial models and risk assessment. Examples include XGBoost, CatBoost, Scikit-learn’s DecisionTreeClassifier.
• Reinforcement Learning Models: Deep Q-Networks (DQN), AlphaGo, and PPO (Proximal Policy Optimization) all use reinforcement learning. This is best when your AI model needs to learn strategies over time. Roombas navigate living rooms and avoid furniture through reinforcement learning.

Step 3: Training the Model

Training is a slow process. Similar to learning a new skill, it’s a continuous loop of exercises, feedback and adjustments. The model continues to improve until it can fulfill its purpose.

1. Input and Processing: The model is fed data (either labeled or unlabeled) for processing.
2. Learning and Adjusting: As the model processes data, it finds relationships and makes generalizations. We give the model feedback to refine its accuracy and decision-making.
3. Tuning: Once our adjustments begin to take shape, we can focus on more detailed ones, tuning. At this stage, the model can already perform many tasks efficiently, but it’s not quite production-ready.

Step 4: Validation and Fine-Tuning

Imagine taking a driving test without ever having driven a car. You passed the written part of Driver’s Ed, but you have no experience. You know that the gas pedal makes the car accelerate and that the brakes stop the vehicle. You understand that the steering wheel allows you to turn. You get behind the wheel and quickly learn that you aren’t ready. You don’t time the pedals correctly, you turn too hard, and BAM! You just had your first car accident. When driving, you don’t just need the theory, you need experience.

During validation and fine-tuning, the model is tested in real-world scenarios. This might include deep conversations, financial modeling, image generation, and more. The model needs to practice and refine its real-world capabilities. During this phase, the developer makes precise adjustments to ensure it runs properly. When stopping a car, you don’t just slam the brakes, you press the pedal smoothly and come to a gentle stop. Similarly, your AI model learns to generate output that aligns precisely with your goals.

Step 5: Testing and Deployment

Would you use a medication that’s never been tested? On paper, everything looks good, but side effects are completely unknown, and its efficacy hasn’t been proven. This sounds kind of dangerous, doesn’t it?

You wouldn’t want to deploy an untested model either. In the late 2010s, poorly trained AIs were deployed to production after training on social media. Proper testing could have prevented the corporate embarrassment and social fallout that followed.

“Build a man a fire, and he’ll be warm for a day. Set a man on fire, and he’ll be warm for the rest of his life—or until the untested prototype explodes in his face.” –Terry Pratchett

Once the model has been rigorously tested, it’s ready for deployment. If a test fails, we make refinements and try again.

Challenges In Model Training

AI training isn’t all sunshine and rainbows. There are many pitfalls we need to avoid. The biggest issues in AI training are actually the same ones that plague software development in general.

• Poor or Tainted Data: If you train a model using garbage, you get a garbage model.
• Weak or Nonexistent Testing: You need to test all possible scenarios. Otherwise, you’ll end up like the guy from the Terry Pratchett quote.
• Black Box Problems: Neural networks are often called “black boxes.” We still don’t fully understand how they work. We know that one neuron fires and talks to other ones. Debugging a neural network is like asking a Neanderthal to perform brain surgery… with a club.

The Future of Model Training

AI training is evolving in ways we didn’t think possible. Today, you can ask an LLM how to build an LLM and it’ll tell you. Soon, AI models will train other AI models directly. It’s a good thing they don’t have feelings, human workers never enjoyed training their replacements.

Thanks to few-shot learning, training data and AI models are shrinking. New, more efficient methods emerge every day. Smarter models are running on weaker hardware. With each training breakthrough, we get closer to the philosophically enlightened toaster… and other more useful things.

Conclusion

Without proper training, AI models crash and burn. We’ve come a long way, but we’re only beginning to scratch the surface. As AI becomes more entangled in our daily lives, the innovations we’ll see in the next 10 years are unfathomable. A couple years ago, ChatGPT 3.5 upended the world, but this was just the beginning. If you’re looking into training your own AI model, take a look at our ai tools.

If you want to procure your own data, take a look at our web scrapers that can provide your model with real-world, on-demand data. Start today with a free trial!