How to train an artificial intelligence (AI) model

A recent study shows that 1 in 5 people use AI every day. From the chatbot helping you budget smarter to the recommendations you get when shopping online, behind every smart feature is a model that’s been taught to recognize patterns and make decisions.  

But those models don’t train themselves. There’s data, structure, and purpose behind their every decision. 

This guide provides an overview of how to train an AI model. It will help you better understand the step-by-step training process and how the technology works behind the scenes. We’ll also explore some real-world use cases for each type of AI model.  

Key points

• Training an AI model starts with a clearly defined problem and a focused use case. Trying to solve too much at once leads to weaker results. 
• High-quality, labeled data is more important than quantity. Small sets of clean, labeled data often go further than a lot of messy input. 
• Choosing the right model architecture depends on your goal and data. Don’t overcomplicate what a simpler solution can handle. 
• Evaluation metrics, like precision and recall, aren’t just for show. They guide model improvements and help determine real-world reliability. 
• Nearly anyone can train AI thanks to low-code platforms, accessible tools, and a growing library of hands-on resources. 

What does it mean to train AI?

Training AI means teaching a machine to recognize patterns in data and make decisions based on what it’s learned.  

Imagine showing a friend hundreds of photos of dogs and cats. Over time, they’ll start spotting the nuances between different breed types—fur patterns, ear shapes, tail length. They’ll get better with each example.  

AI models work in the same way, but instead of instincts, they use math. They look for patterns in massive datasets and adjust their internal “rules” until they can reliably predict the right answers. This process of input, feedback, and refinement is called training. 

At its core, training an AI model involves feeding it data, measuring its performance, and refining until it produces dependable results. Do this enough with the right data, and you end up with a system that can navigate complex tasks like predicting fraud, recommending a playlist, or even guiding someone through filing their taxes. 

Common learning methods in AI training

There’s more than one way to teach a machine. The method you choose depends on the kind of data you have and what you want the model to learn. Here are the three main approaches used in training AI models: 

• Supervised learning is ideal when you have clear, labeled data and want reliable results. Think fraud detection or image recognition. Another example would be a spreadsheet of labeled data, like emails marked “spam” or “not spam.” Over time, the model learns to predict those labels on new data. 
• Unsupervised learning skips the labels. Instead, it looks for patterns on its own, grouping similar data points together. Imagine sorting a bunch of photos based purely on which ones look alike, without knowing the subjects. Its strengths with pattern recognition make unsupervised learning good for analyzing customer behavior. 
• Reinforcement learning is more trial and error and does best in changing environments. The model interacts with an environment, gets feedback in the form of rewards or penalties, and slowly figures out the best strategy. This dynamic processing makes reinforcement learning a good choice for robotics and financial decision-making.  

6 key steps to train an AI model

The process of how to train AI follows six essential steps. It all starts with the problem you’re solving. From there, each problem AI attempts to solve means gathering the right data, picking a model that fits the job, and teaching it through iterative learning. Once training is done, you test its performance and refine it to improve results. 

Here’s a closer look at the process. 

1. Define the problem and use case

To successfully train an AI model, you need a clear goal. So, start by identifying the problem you’re trying to solve. Maybe that’s spotting fraudulent charges, recommending a new playlist, or helping someone build a budget. Defining a clear objective shapes the training process, dictating the data you collect and the model you choose. 

A narrowly defined use case also keeps your scope in check. Instead of building a model that tries to do everything, you train it to do one thing really well and build from there. 

2. Understand your data needs

Start by defining what your model needs to learn. If it’s recognizing images, for example, you’ll need a large set of labeled photos. If it’s predicting trends, structured spreadsheets with historical data might be a better fit. Public sources like Kaggle, Hugging Face, or open government databases are great places to start. 

When preparing your data, organize it in a clear, consistent format. For example, you could use CSV files for tabular data or JSON for text and labels. 

Labeling matters. Each data point should match the outcome you’re training the model to predict. Balanced, well-labeled data gives your model a stronger foundation. 

3. Collect and prepare quality data

Once you know your data needs, focus on accurate, relevant examples. After all, AI models are only as smart as the data they receive. If your data is messy or too limited, your model will reflect that. 

To set up your model for success, organize the dataset into three distinct parts: 

• Training set: Used to teach the model 
• Validation set: Used to tune it during training 
• Test set: Used to evaluate final performance 

Keep the data you use to train an AI model clean and well-structured. The more diverse and representative your dataset, the better your model will generalize to real-world scenarios. 

4. Choose an AI model architecture 

Once your data’s ready, it’s time to pick an AI model—the algorithm your AI will use to learn patterns and make predictions. The model you choose should align with your objective and the structure of your data. 

Here are a few common use cases and model types: 

Classification tasks: Used to classify things, like flagging spam emails or detecting fraudulent transactions. 

• Logistic regression is a simple algorithm that predicts binary outcomes (like yes/no or spam/not spam). 
• Decision trees map decisions and outcomes based on input features. 
• Random forests combine multiple decision trees for more accurate predictions. 

Image or language tasks: For tasks like image recognition or language translation. 

• Neural networks are designed to process visual data like photos. 
• Transformer models power large language tools (like ChatGPT) by analyzing text in context. 

Prediction tasks: If your goal is to predict a number (say, a house price). 

• Regression models predict continuous values (values that fall on a scale, like price, distance, or time) based on input variables. 

You don’t need to build these from scratch. Depending on your technical expertise, consider using tools like: 

• Scikit-learn makes it easy to test basic models like linear regressions or decision trees. 
• TensorFlow and PyTorch are more powerful frameworks used for training deep learning models. 
• AutoML tools (like Google’s Teachable Machine or Azure ML Studio) help non-developers get started with minimal code. 

At this stage, the goal isn’t perfection. It’s to pick a model that’s a reasonable fit and start training so you can learn and iterate. 

5. Train the model

With your data and model in place, it’s time to train. Feed the model the training data in batches. From there, it processes the input, makes predictions, and compares those predictions to actual results. The gap between prediction and truth is what the model uses to improve. This is called loss. 

To reduce that loss, the model adjusts its internal weights (tiny values that shape how the model makes decisions) to help it perform better in the next round. This adjustment process is called backpropagation. This cycle repeats across multiple epochs (full passes through the training data) until the model gets consistently better. 

Use tools like TensorFlow or PyTorch to set learning rates and control your model’s progress. Depending on your dataset and model size, training can run in a few minutes or require hours on a general processing unit (GPU). 

6. Evaluate and improve the model

An AI model is only useful if it performs well in the real world. Once your model is done learning, it may produce decent results. But constantly evaluating and refining its output is where the real magic happens.  

Test your model using metrics like:  

• Precision: How many of its positive predictions were right 
• Recall: How many actual positives it found 
• F1 score: A balance of the two 

These help you understand if the model is sharp, sloppy, or somewhere in between. 

This is a continuous process. As outputs get better, you continue to go back and tweak. Maybe the data needs adjusting. Maybe a different model of architecture works better. Training AI is rarely one and done; it’s a cycle of improving on previous outputs. 

Let’s say your model catches 95% of spam emails (high recall), but only 60% of the emails it flags are actually spam (low precision). That may be acceptable if your goal is to catch as much spam as possible, even if it means a few false alarms. But if you’re building a medical diagnostic tool, false positives can carry real consequences, so precision becomes the priority.  

Ultimately, choosing the right metric depends on what matters most: catching every possible case, avoiding false positives, or striking a balance with the F1 score. 

Real-world use cases for trained AI models

Once trained, AI models can be put to work in ways that touch nearly every part of modern life. They power customer service chatbots that respond instantly and improve with every interaction. They drive intelligent assistants like Intuit Assist, which help users navigate complex financial decisions by analyzing data in real time. 

They’re also critical in fraud detection. AI models learn what normal behavior looks like and flag anything that strays from the pattern. That could mean catching a suspicious credit card charge or spotting unusual login activity across accounts. It happens fast, often in real time, providing for stronger and smarter cybersecurity. 

These models are working behind the scenes in tools people use every day, turning raw data into useful, timely actions people depend on. 

Can you train an AI model without experience?

You don’t need to be a data scientist or have a Ph.D. to train AI. Thanks to low-code tools, open-source platforms, and cloud services, you can start learning and experimenting with minimal experience. 

Platforms like Google Colab, Amazon SageMaker, and AutoML enable beginners to hone their AI skills with sample datasets and pre-built models you can tweak and retrain. Many even run in your browser, with no setup required. 

You’ll still need curiosity and patience, especially when things don’t work on the first try. But online courses, tutorials, and tools like Intuit’s free AI education content can help you build confidence quickly and pursue popular AI careers, like becoming a prompt engineer. 

Where AI skills can take you

Learning how AI works doesn’t just expand your knowledge. It unlocks new career paths. The demand for people who understand how AI really works is growing fast. 

Building and training AI models is a skill anyone can learn. As you grow your knowledge, you’ll start to see where it can take you.  

If you’re exploring what’s next, take a look at AI jobs at Intuit. You might find a career path that matches your interests and offers a fulfilling future doing the best work of your life.