AI models of different complexity levels have been in business use at least since noughties. As the technology evolves, data amounts rise, and computational power grows, the AI models are getting increasingly smart, expanding the number of their business applications.
AI models are now able to automate, reinforce, and expedite a whole host of processes across industries. These include all kinds of data analysis (from credit scoring and fraud detection to medical diagnostics), customer service and marketing tasks (from handling cases and recommending items to creating varied content), as well as management and decision-making (from demand forecasts and inventory optimization to risk analysis and customer behavior prediction).
In this article, we'll explain what's under the hood of AI models, which types of them suit different use cases, and how to make an AI model benefitting your organization.
Table of Content
1. What’s an AI Model?
It's not so easy for a non-technical audience to wrap its head around AI and the wealth of its subsets and terms. To dive deeper into the realm of AI models, we'll start by clarifying some key notions like AI, ML (machine learning), and DL (deep learning).
Artificial intelligence (AI) is an umbrella term for a bunch of technologies in computer science with a focus on developing smart systems/machines operating closer to the way a human brain does.
An AI model is an algorithm/set of algorithms (we can also call it a program) fed with data and learning from it to find patterns as well as make predictions, classifications, and decisions without being strictly programmed for every single task. There are many types of AI models by structure and ways they're trained. Most AI models used in business today are either ML or DL-based.
1.1 Machine Learning Models
Machine learning (ML) is one of the largest subsets of AI. It implies creating algorithms able to mimic human cognitive processes: learning from data, noticing patterns, and making decisions based on the gained experience. As the ML model is being trained on data, it progresses in the accuracy of its insights.
Generally speaking, ML models are simpler and less computationally intensive compared to DL ones. ML models (linear regression, decision trees, k-nearest neighbors, and others) are widely utilized for tasks like forecasting, clustering, classification, segmentation, and so on. The classic examples of ML models are recommendation systems in online stores or predictive analytics tools.
1.2 Deep Learning Models
Then goes the offshoot of ML, DL. Deep learning involves training computers using models even more resembling the human brain. These are so-called neural networks containing multiple layers of interconnected artificial neurons (also called nodes). These neurons are connected by synapses carrying weights. As the model trains and learns from prediction errors, the weights are adjusted to improve accuracy. The more layers (their number ranges between 1-3 and thousands), the deeper the neural network is and the more precise its outputs are.
DL models (like convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and others) are far more complex than ML ones, demanding much larger datasets and significant computational resources to perform tasks like image recognition and natural language processing. Some typical examples of DL in action are speech transcription, medical imaging analysis, and content generation (generative AI models).
1.3 Machine Learning Paradigms
AI modes are trained using different methods (so-called machine learning paradigms) and their combinations. Here are the three most popular of them:
- Supervised learning. In this case, an AI model is trained on a labeled dataset, meaning each training example is paired with a desired output value. Learning the relationship in these sample pairs, the model trains to map inputs to the correct outputs on new data.
- Unsupervised learning. In this paradigm, input data fed to an AI model doesn't have labeled responses. Therefore, the model tries to discover the structure and patterns of the data on its own.
- Reinforcement learning. In this type of machine learning, the model trains to make the right decisions in interaction with a dynamic environment. The machine needs to achieve certain goals and is given feedback (either rewarding for desired outcomes or negative for undesired ones). This trial-and-error process makes the AI model perform increasingly better over time.
Now that we're acquainted with the basic artificial intelligence terms let's explore some AI models in more detail, focusing on how they can be beneficial across industries.
2. Popular AI Models & Their Business Applications
There are dozens of AI models able to power business tools and aid with numerous operations. Some tasks require an advanced DL model, while others aren't so demanding, and a simpler ML model is enough. In this chapter, we'll take a look at some widely used artificial intelligence models and learn more about how they work and how businesses can apply them. Let's start with machine learning AI models utilizing the supervised learning paradigm.
2.1 Linear Regression & Logistic Regression
Linear regression is used to predict certain numerical values (e.g., monthly sales) based on the relationship between different input factors (e.g., the amount of money spent on ads). Linear regression models analyze past data to find a straight-line relationship between input and output variables (e.g., the amount spent on ads and the resulting sales).
Major applications: all sorts of sales and financial forecasting, marketing analysis, and risk assessment.
Logistic regression is used for binary classification problems, where the outcome is one of the two possible values (yes/no, true/false, 0/1). Having analyzed the available data, these models predict the probability that a given input belongs to either of the two categories.
Major use cases: customer churn prediction, credit scoring, spam/fraudulent activity detection.
2.2 Decision Trees & Random Forests
A decision tree is a model utilizing multiple if-then statements for categorization, decision-making, and predictive modeling. It's a flowchart-like (or tree-like) algorithm basing each next action on the answer to a previous question.
Major use cases: simple customer support or healthcare chatbots, segmenting customers, product recommendations.
A random forest contains a bunch of decision trees, with outputs from each of them being merged into the final prediction or decision. Random forests produce more accurate results than a single decision tree.
Major use cases: personalized product recommendations, disease outcome predictions, inventory levels optimization.
Now, let's move on to the deep learning model types. All these models are based on neural networks and can effectively simulate human intelligence in many cases, even outperforming human cognitive function in some tasks.
2.3 Large Language Models (LLMs)
LLMs (like the most renowned one now, GPT(generative pre-trained transformer), are artificial intelligence models designed to handle a broad spectrum of language-related tasks, from understanding and processing human language to generating meaningful human-like content. These models are based on advanced neural network architectures (e.g., transformers) and are trained on ginormous amounts of textual data.
Major use cases: content production, translation, text summarization, virtual assistants.
2.4 Convolutional Neural Networks (CNNs)
CNNs are AI models geared for processing and analyzing visual data. They are a series of layers (called convolutional, pooling, fully connected, and activation layers) working together to identify, recognize, and learn patterns in images or videos.
Major use cases: image classification, facial recognition, object detection, medical imaging analysis, quality control.
2.5 Recurrent Neural Networks (RNNs)
RNNs are artificial neural networks for sequential data processing (e.g., time series data, text, audio). An RNN model has a built-in memory, meaning it remembers previous inputs and uses this context to process the current ones. This allows it to make better outputs (decisions) based on both the current and past inputs.
Major use cases: customer service chatbots (see some modern ), financial forecasting, predictive machinery maintenance.
2.6 How Do You Choose an AI Model?
AI development is truly a huge field. There are many AI models we haven't mentioned: k-nearest neighbors (KNNs), k-means clustering, generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, and more.
You might have noticed that use cases for some ML/DL models overlap. Then, which type is the most suitable for your company? It depends on a few factors, including exactly which problems you want the model to deal with, how complex the model should be, and what resources are at your disposal in terms of data, computation, and budget.
The optimal way to navigate the intricacies of AI models is to have AI dev pros on board. An agency like Onilab will aid in choosing the right options and building AI models for your business objectives. Below, we'll discuss the ways to get an AI system and the steps to implement it.
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3. How to Create an AI Model for Your Org?
AI models are now indispensable for businesses, from manufacturing to healthcare, from agriculture to retail, from finance to education. Companies often employ several models and can do so in a few ways.
A very popular (and quite cost-effective) path is integrating and fine-tuning a pre-trained AI model using domain-specific training data to get better results for a particular company and use case. Many players, including giants like OpenAI, Google, IBM, and Meta, offer their powerhouse AI models to be customized this way. There are some open-source options available as well.
For many orgs, having ready-made tools with AI-powered functionality will be sufficient. All major business apps like Salesforce, HubSpot, Google Analytics, Tableau, Power BI, SEMrush, and others have been adding more and more AI and gen AI capabilities by the year. Indeed, it's the most accessible and affordable option, especially for SMBs.
Finally, to cover specific needs feature- and data privacy-wise, there's an option to build an org's own AI model. The process can include extensive AI model training, but, in many cases, fine-tuning and creating a custom app is enough. So, let's see what the custom building of an AI system looks like.
3.1 Identifying the Model's Business Purpose
As trivial as it may sound, setting concrete objectives is a foundational step in developing an effective AI model. To determine the best-fitting machine learning or deep learning model, outline the pool of problems/tasks the tool needs to solve, and the business processes it's meant to automate/optimize/accelerate.
Step zero is the right time to decide who will be in charge of the whole project. AI consultants, data scientists, and developers will then help address other important questions of this phase, like the approximate data and budget requirements and how long it all will take.
3.2 Preparing Training Data
Quality training data is key to success in model training and fine-tuning. Plus, the more data you have, the better. So, you need data scientists or seasoned analysts/developers for meticulous input data preprocessing.
Data science specialists will define what to gather (relevant data types), where (real business data, public datasets, web scraping data, synthetic data, etc.), and how much. Then, they'll proceed to data collection and data cleaning: raw data (especially unstructured data, but often structured data, too) needs to be carefully revised and put to order.
Data processing in AI modeling is frequently associated with several challenges: ensuring tight data security, compliance with data privacy regulations and intellectual property rights, and ethical considerations like biased data.
3.3 Selecting the Right AI Model
The next step is deciding which AI model is optimal for your business needs (or there can be several models). A machine learning or deep learning model? Which algorithm (architecture)? Take a pre-trained model and customize it, or build one from the ground up?
Once the team is positive about the choice of the model and the way to get it, it moves to planning the work scope and designing the tech environment for the development and training process: servers, APIs, frameworks, libraries, data warehouses/lakes, and so on.
3.4 Training/Fine-tuning the AI Model
This stage contains several subtasks: splitting available business data into training and testing sets, followed by training itself, and several iterations of system adjustments. The data science team starts with splitting existing data into training (60-80% of data), validation (10-20%), and testing (10-20%) sets.
Using the first dataset, the model trains to recognize patterns and relationships within data and create outputs. The second set is used to tune hyperparameters (settings controlling the model's training process) to improve architecture and performance. In particular, the validation dataset helps to detect overfitting, one of the common challenges during training. The overfitting issue occurs when the model learns the training dataset too well, leading to good results only with the training data and reduced accuracy with new data.
The testing set is needed at the end of training to check the overall system's accuracy and generalization abilities on fresh data unseen by the AI model. After more adjustments and refinements, the model is good to go.
3.5 Deploying the AI Model
The last stage of the AI project is deploying the model for business users. To do so, we either integrate AI models into existing tools or build AI-powered custom apps. So, this phase may involve designing and developing web/mobile app logic and interface, connecting the model with the rest of the tech environment (business apps, databases, etc.), QA, launch, and monitoring to ensure everything works seamlessly and smoothly.
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