A Guide to AI Models: What They Are and How They Work

AI models offer tons of potential to businesses across industries, but there’s a lot to learn to create, train, and deploy the right AI model. Here, learn the basics of types of AI models, how they work, and how to choose the right ones.

What are AI models?

Artificial intelligence (AI) broadly refers to the idea that machines can do tasks that have typically required human intelligence. An AI model is a specific computer program trained on a set of data to act on its own without human involvement. There are multiple types of AI models, which can analyze data and identify patterns, automate processes (especially around decision-making), generate content, and make predictions. AI models depend on accurate, healthy data to learn and operate.

How AI models work

An AI model might use any number of different algorithms to achieve the task it was programmed for. AI models incorporate algorithms, which are procedures applied to a set of data to complete a function. Once an algorithm (or a combination of algorithms) has been applied to a dataset, the AI model is the output. The model uses that algorithmic logic to make its predictions or decisions. 

Underlying AI models are some common algorithms, each with its own particular specialties. Users building their own models can consider which will work best in each situation. Common algorithm types include linear regression, logistic regression, decision tree, random forest, and support vector machines (SVMs).

An AI model will be dynamic and data-driven, since it can use its learned logic over time to become more accurate and tailored to their purpose. AI models learn from input data to become more useful to humans, whether completing simple tasks or executing complex workflows, and can be updated continually with fresh data.

Building an AI model typically requires three steps:

• Choosing and collecting the data. Decide which datasets the model will be trained on.
• Training the model. Enter that data into an algorithm to learn patterns and relationships.
• Deploy the trained model. Test your model to ensure it can predict outcomes or automate decisions accurately. 

Depending on the industry, the AI model could be applied to many activities. You may train the model on sales data, for example, then ask it to make predictions or identify useful customer insights. For IT teams, AI can automate ticket resolution to cut down on technician workloads.

Understanding ML vs. DL vs. LLMs

ML, DL, and LLMs are common terms in the world of AI. Each of these plays a role in how AI models work. Here’s more on each of these concepts.  

Machine learning (ML) 

Machine learning is a category within AI. As its name suggests, ML is a way for machines to learn without relying on manual human coding. ML depends on algorithms and datasets to learn from past experiences to make predictions and decisions and improve over time. AI and ML terminology may be used interchangeably, but they aren’t the same; simple AI models exist that are not ML, such as knowledge graphs that are a series of if-then-else statements. Only AI models capable of machine learning can optimize performance over time without help from humans. 

So, all ML models are AI, but not all AI is ML.

There are different ways that ML models learn over time to make conclusions or predictions. Learning methods include supervised learning, unsupervised learning, ensemble, and reinforcement learning. ML algorithms show up for uses like TV and movie recommendations on a streaming platform, or to identify fraudulent transactions for a financial institution.      

Deep learning (DL)

Deep learning, or DL, part of the field of ML, also learns from data to improve its performance over time. But deep learning uses neural networks, which aim to mimic human behavior and intelligence, to improve its performance over time. Neural networks are made up of interconnected nodes designed to serve as artificial neurons that work together like the human brain. Deep learning can be useful for tasks like image and speech recognition and natural language processing. The photo search function on your phone, for example, likely relies on deep learning to identify people, pets, or other objects.

Types of AI models

Types of AI models vary beyond neural networks and LLMs, though those have gotten the most press in the past few years. Here are the commonly used and discussed models.

Large language models (LLMs)

Large language models, or LLMs, are a type of deep learning model designed specifically for natural language processing (NLP) tasks. LLMs all use neural networks to identify and model complex patterns in language. Many of the emerging AI use cases we’ve seen recently depend on LLMs: text generation, language translation, sentiment analysis, text summarization, writing code, knowledge retrieval, and more. LLMs have been trained on massive data sets so they can perform tasks like responding to queries or predicting the next word or phrase when a user is typing. 

Neural networks, and thus LLMs, learn by adjusting internal settings when they discover differences between their prediction and the result. The network gauges how far the prediction was from the correct answer, then changes the settings to make future predictions more accurate. 

Foundation models

These pre-trained AI models can be an easy way for businesses to get started as an out-of-the-box option. Foundation models can perform a set of tasks and have been trained on massive datasets using neural networks. A company could use just one foundational model to accomplish a range of daily tasks like text generation, classification, and more.

Diffusion models

This type of generative AI model has become known for image generation, such as creating synthetic photos or videos that look realistic. These models can edit existing images or create net-new ones. Diffusion AI models can also be used for image restoration, molecular design, and more.

Multimodal models

Multimodal AI models can process information from multiple formats, such as text, images, and audio. Accessing multiple types of data helps the model deliver more precise answers and predictions, and many industries can benefit from multimodal models.

Custom-built models

Though you may start with a foundation model, building a custom AI model could be the best way to solve a particular business use case. Building from scratch starts with capturing the right data and choosing the preferred algorithm, then choosing a framework like TensorFlow or PyTorch, training the model, and fine-tuning it. 

Understand the process of building an AI model

Building or adopting an AI model can have a huge impact on your business, saving time and resources to be better spent on innovation. But where should you start with developing an AI model? Here’s what to know.

Identify the problem

Before you start developing an AI model, decide which problem the business needs solved, and categorize it accordingly. Consider both input and output, such as whether you’ll have access to well-labeled data or not. This will help narrow down which algorithms to use and type of learning.

Evaluate and prep the data

High-quality, relevant data is essential to building and using successful AI models. Make sure the data to feed the AI model is consistent, unbiased, validated, and highly relevant to the problem being solved. Too little data, or low-quality data, and the AI model won’t have enough to learn from, leading to more inaccurate predictions. Preprocessing or cleaning the data up front can lead to much better outcomes later.

Pick the right algorithm and model complexity

The right algorithm for your business’s AI model is the one that will improve performance. Look at metrics like accuracy, precision, and recall when gauging performance. And consider your resources, both financial and expertise-related, to understand whether a simple or more complex AI model is right for your needs. There’s a big difference between generative AI and agentic AI, for example. Consider much data you’ll need as well as the total number of features desired for the model.

See what training is needed

AI model training can be arduous, especially if it’s a deep learning model. It can require a lot of time and cost to train a model, particularly if accuracy is the highest priority. Get to know more about the main types of AI model training processes, which include supervised, unsupervised, and reinforced. Consider the business goal and whether a foundational or simpler model can meet your needs.

How to refine an AI model 

Once you’ve trained the AI model, gauge its effectiveness and performance before launching it. It’s a best practice to divide the dataset into three parts: the largest is the training set for the model; the validation set is used for fine-tuning the model; and the testing set, to conduct an evaluation of the AI model after it’s trained and fine-tuned. After validating the model, perform any optimization and use the testing data to replicate possible scenarios and make sure it’s ready to go.

Keep an eye out for these common issues when you’re developing and launching AI models. 

Prevent AI hallucinations

AI hallucinations refer to incorrect or misleading results generated by an AI model. When this happens, examine whether there is sufficient training data, incorrect assumptions made by the model, or bias present in the data. During training, use regularization to limit the number of outcomes the model can predict. And only train an AI model on relevant data to avoid incorrect predictions.

Be prescriptive with model training

Train an AI model with a template to follow, such as a copy template to guide the model in its predictions. In addition, give specific feedback to an AI model when it first generates text so it can learn what you are looking for. To avoid bias, ensure that datasets are diverse in their representation and conduct regular audits of AI output.

Tune accordingly

Both underfitting and overfitting are possible when fine-tuning an AI model. Underfitting refers to a model failing to capture the complexity of data. In that scenario, it performs well with small amounts of data but doesn’t perform in real-world situations. Overfitting happens when a model learns the training data too well and then isn’t able to generalize when it needs to. Ideally, the AI model will have a balance between these. Make sure to fine-tune parameters and try optimization techniques.

Continuously update the data and model

An AI model is an entity that changes as data does. So, make sure to track performance metrics and get feedback from users to make sure the model is continually working for your business. Optimize the model when new data emerges or data patterns change.

Once you’ve gotten a handle on AI models, consider whether AI agents can be useful for your business. An AI agent is a goal-driven system that can sense its environment, decide on the best action, and execute it. These can be hugely helpful for teams that work on repeated processes with many of the same tasks.

How AI models are transforming industries like IT

Massive increases in available compute and storage, plus a big dose of human ingenuity, came together to put AI models into the spotlight. In just a few years, AI has dramatically transformed work and business. 

For IT teams, purpose-built AI models and agents can reduce workloads and improve response times for users. This includes actions beyond the chatbot to take autonomous actions like diagnostics, scripting, and ticket resolution. Atera’s AI Copilot serves IT technicians to help speed up user support and focus on resolution. 

AI Copilot is an advanced assistant that can create custom scripts, summarize remote sessions, search devices with natural language, gauge ticket sentiment, respond to end user queries, generate knowledge base articles, troubleshoot devices in real time, and other activities that are well-suited for AI models to solve.