in Blog

October 04, 2024

How to Make an AI Model: A Practical Guide for Enterprises

Author:




Artur Haponik

CEO & Co-Founder


Reading time:




12 minutes


According to Forbes, approximately 73% of businesses use or are planning to use AI for their day-to-day operations [1]. With widespread applications, from automating routine tasks to content creation, customer service, and fraud detection, AI is slowly becoming a must-have technology for businesses looking to stay competitive.

If you plan to integrate AI into your operations, you can either buy a pre-built AI model that performs basic functions or build a custom one for operations, challenges, and objectives unique to your business.

In this post, we’ll show you how to build an AI model for your business and what to consider along the way. Let’s have a look.

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Different categories of AI models

An AI model is a program or algorithm that’s been trained on a set of data to recognize certain patterns and trends and make accurate predictions based on user prompts. AI models are mainly classified into two main categories based on their learning paradigm and model architecture:

Models based on learning

AI models based on learning function by learning. This means developers don’t define any relationships or patterns in the data. Their only work is to feed the AI model random data, which learns by identifying patterns and trends in the data. AI models based on learning include:

Supervised learning models

A supervised training model is trained using labeled datasets as training data. You (the developer) consistently feed the model with these datasets, and it establishes patterns and relationships according to their labels. After training, you can prompt the model and check whether it gives the desired output. If it gives the wrong output, you provide feedback and train the deep learning model with more datasets until it gets it right. Hence the name “supervised.”

Unsupervised learning models

Unlike supervised learning AI models, unsupervised models don’t require human input. Instead, they use self-learning algorithms and establish their own rules based on the data you feed them. They’re incredibly flexible in recognizing hidden patterns or groupings within the data without predefined labels or instructions.

Semi-supervised learning models

These are a mix of supervised and unsupervised learning models. They use small unlabeled and large labeled datasets for training. The model starts by learning from a small amount of labeled data to help it understand the large unlabeled data sets.

Reinforcement learning models

With reinforced learning models or Reinforcement Learning From Human Models (RLHFM), learning is accomplished using a reward and punishment mechanism. The AI will make predictions and decisions and receive feedback. Correct predictions are rewarded, and incorrect ones are punished. They’re especially useful in video game AI development and robots that mimic human activities, like robot waiters.

AI models based on task type

These are AI models that are classified based on the tasks they accomplish. The tasks they can accomplish can be routine, formal, and expert. Below is a brief look at the nature of these tasks:

  • Formal tasks: In the AI domain, formal tasks require using logic and a bit of learning
  • Expert tasks: These are knowledge-based tasks centered on information with little need for sense
  • Routine tasks: Routine tasks are also knowledge-based but require complex algorithms and representations.

With that in mind, there are seven types of AI models based on task type, namely:

Classification models

These models classify output variables into different groups. They use algorithms to tag data input pieces, categorizing them into predefined labels. Useful applications for these AI models are in spam detection, medical diagnosis, and sentiment analysis.

Regression models

With regression models, developers use algorithms to identify linear relationships and predict output values. These algorithms identify relationships between real or continuous variables to make accurate predictions or correct decisions.

Clustering models

As the name suggests, clustering models group similar data into clusters. In doing so, they can identify patterns, trends, and relationships without labels. This makes them super useful for exploratory applications and segmentation analysis.

Generative models

Generative machine learning models process copious amounts of data to generate synthetic data. They learn from the underlying distribution of a dataset and generate new data points that resemble the original dataset. They can produce text, images, audio, and video output based on user prompts. Examples of generative models include ChatGPT and MidJourney.

Dimensionality reduction models

These models simplify higher dimensional data by removing irrelevant data and reducing the dimensions while retaining the data’s original meaning. It uses data compression to integrate the different features using either linear or non-linear dimensionality reduction.

Recommender systems

These AI models learn from historical data to uncover trends and patterns. That way, they can make accurate recommendations. They’re especially useful for retail platforms, but you can also use them in entertainment to suggest movies and social media to suggest friends.

Learn more about Multimodal AI Models

Key components needed to build an AI model

Building an AI from scratch requires several key components for maximum effectiveness. First, you need to clearly define the model’s value proposition and objectives. You also have to decide the best technique to build your AI model. The technique options you can choose from include:

  • No-code/low-code platforms: This is an AI development paradigm that doesn’t use code or uses minimal code to develop. A good example is Google AutoML, which lets you create your AI model using pre-built algorithms and drag-and-drop features.
  • ML Libraries and Traditional Programming: Here, you use traditional coding languages like Python and refer to machine learning libraries like Pytorch and Keras to build your model.
  • AutoML: With AutoML (automated machine learning), the AI development platforms automate the AI model creation process. The platform will handle everything from data preprocessing to fine-tuning. The need for little human intervention means faster model deployment and fewer errors.

That said, AI models are composed of conceptual layers, which form the backbone of your AI system. Below is a brief breakdown of these conceptual layers:

Infrastructure

This describes the computational hardware and software required to build the AI model. Infrastructure also consists of other categories, namely:

  • Processing infrastructure
  • Network Infrastructure
  • Storage infrastructure
  • Application layers

Hardware elements like graphics processing units (GPUs) and servers are crucial for managing and accelerating AI computations. On the software end, you should explore technology stacks like PyTorch and TensorFlow.

Data

The most critical component of any AI model is data. Remember, you must train your AI model consistently with high-quality data to ensure accurate output and reliable performance. You’ll need infrastructure to store, manage, and process the data. You must also clean, transform, standardize, and improve the data to meet quality requirements.

Depending on your AI model scale, you’ll need to tap into data storage devices, databases, data warehouses, or lakes. You also need a comprehensive system for feeding the data, processing it, and governing it in accordance with data protection regulations like the California Consumer Privacy Act (CCPA) and Health Insurance Portability and Accountability Act (HIPAA) [2]

Model

This layer is the most important part of your AI project. The model is a combination of algorithms that you use to create and train your AI model. You’ll also need to design neural network layouts consisting of different nodes, which are interconnected processing units that allow the AI model to learn.

Service

The service layer is the interface that lets applications and infrastructure interact with the AI model. It’s usually the last layer before deployment and facilitates the model’s management in the real world, transitioning from a matrix of data values and figures to actual usage. Communication and integration is achieved using Application Programming Interfaces (APIs).

Application

The application layer determines how to use the AI model in business procedures and incorporate it into existing software. The AI model will make predictions and suggestions to help you make more informed decisions about your procedures.

Integration

The final service layer involves integrating the AI model into business operations and applications. Usually, a Large Language Model (LLM) is the foundation of this deep learning layer. The LLM enables natural language understanding so your business can use the AI model to perform different tasks.

A step-by-step guide to creating an AI model

Building an AI model requires diligence and a careful, systematic approach for the best results. Here’s how you can build an AI model from the ground up:

Step 1: Preparation

Step 1 involves preparing the model’s foundation. To do so, you’ll need to answer the following questions.

  • Why does my business need a custom AI model?
  • What does the model aim to accomplish?
  • Does my business have the necessary resources for this project?
  • What are the potential risks and ethical considerations?
  • Do I have the necessary data for this operation?
  • What extra data might I require?

Step 2: Data collection and preprocessing

As mentioned, building an artificial machine model requires plenty of data. After preparation, the next step involves collecting high-quality data from your databases. Next, you must organize and clean the data using data handling tools like Scikit-learn and Python’s Panda. These are incredibly effective at identifying missing entries and removing redundant data.

Step 3: Selecting the model and initial data

After screening the data and verifying its quality, the next step involves selecting the model. This isn’t as simple as choosing a model from a list. Instead, it involves using programming languages like Java and R to implement algorithms and techniques suited to your needs. Next, you’ll have to set up the model by defining its architecture. You might have to resort to cloud services like Amazon AWS or Google Cloud for scalability requirements.

Step 4: Model training and optimization

Next, you’ll apply the techniques and algorithms you chose to the data to train the model. You’ll also need to establish hyperparameters and cross-validation techniques to help you identify the best configuration. Afterward, use linear regression and similar techniques for mapping correlation between data sets. For building pre-trained models, consider using TensorFlow and PyTorch (if you used Python in Step 3) since they allow better fine-tuning.

Step 5: Testing and refinement

This step helps determine whether the AI model can achieve the objectives you outlined in Step 1. You’ll use a test dataset to check whether the AI system produces the expected output. Next, identify the areas that need improvement. For instance, the AI system may struggle with specific data types or underperform when predicting certain patterns.

After pinpointing these areas of weakness, you must make necessary adjustments to address these issues. These adjustments may involve tweaking the hyperparameters or adding new deep-learning features. In some cases, you might have to overhaul the entire algorithm. Test the AI system again with a new dataset and repeat the process until you get the desired results.
The last testing phase involves conducting User Acceptance Training, a very crucial aspect of AI development [3]. This involves having actual end-users (usually employees) test the AI system and gauge its performance in real-world use. This will give you insights into the deep learning model’s functionality and overall usability. Lastly, prepare all the necessary documentation and include the adjustments you made before moving to the next step.

Step 6: Deployment and maintenance

If the AI model checks all the boxes, the last step in the AI development process is deploying the AI system. You can do so by integrating the system with current APIs for efficient interoperability. Alternatively, you can invest in new interfaces to allow users to interact seamlessly with the machine learning system and utilize them in their daily operations.

How to Make an AI Model – FAQ

Is It possible to create a custom AI model for my business?

Creating a custom AI or machine learning deep learning system for your business is possible. However, the process is resource-intensive and requires extensive computational hardware and software. You might also have to hire skilled technicians to handle the technical aspects of artificial intelligence development. Alternatively, you can outsource these processes to a managed IT service provider.

What are the ethical implications of knowing how to build an AI model?

The data you use to train the artificial intelligence model may sometimes have societal prejudices based on gender, race, or nationality, among others. These biases may reflect in your AI systems and paint the wrong picture. Therefore, you should stay vigilant to ensure your artificial intelligence systems remain fair, unbiased, and inclusive by training your deep learning model with high-quality data from reliable sources.

Privacy is another major ethical concern when building an AI model. Ensure your data collection, storage, and processing methods align with applicable data privacy regulations. Failure to do so may lead to hefty fines and a tarnished public image. Plus, it’s your moral obligation to ensure the security and privacy of users’ data.

How do you choose the best artificial intelligence algorithm for your AI system?

To choose the best algorithm when building an AI model, you’ll have to examine factors like your accuracy requirements, the complexity and structure of the data, the parameters, and the variables. You should also consider your computational resources and whether they meet the minimum requirements for the deep learning algorithm you have in mind.

Wrapping up

Now that you know how to build an AI model, it’s up to you to create one that suits your business needs and achieves your bottom line. Remember, you don’t have to spend a lot of money to create an effective AI system. You can utilize AutoML platforms, which are not only affordable but also require minimal human input.

It’s also worth noting that deep learning tools require significant investment, but you don’t have to meet these upfront costs. You can always outsource the computational requirements to a managed service provider and reap the benefits of these deep learning systems without the hefty costs.


[1] forbes.com, How Businesses Are Using Artificial Intelligence in 2024, https://www.forbes.com/advisor/business/software/ai-in-business/, Accessed on September 26, 2024
[2] hss.gov, Summary of the HIPAA Privacy Rule, https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html#:~: text=The%20Health%20Insurance%20Portability%20and%20Accountability %20Act%20of%201996%20(HIPAA,and%20security%20of%20health%20information., Accessed on September 26, 2024
[3] devops.com, What Is User Acceptance Testing and Why Is it so Important?, https://devops.com/what-is-user-acceptance-testing-and-why-is-it-so-important/, Accessed on September 26, 2024



Category:


Artificial Intelligence