Blog Summary:

This blog is an exclusive guide for startup and enterprise owners that will provide valuable information on everything they want to know about How to Build an AI model. It will take them through the types of AI models, the important architectural layers of an AI model, and a six-step process for starting to build their custom AI model.

Today’s conversations around AI are more about how companies who want to embrace AI can outperform those who don’t. The global Artificial Intelligence market is expected to reach USD 827 Billion by 2030.

With the right team of data scientists and strategists, building an AI model can provide an automated system to handle routine tasks and propel the decision-making process forward.

However, finding high-quality data from massive volumes of datasets is very similar to finding a needle in a haystack. Almost 70% of CEOs are concerned about AI delivering biased outcomes because they don’t have the right datasets.

Whether you’re an e-commerce business owner or a manufacturer, even a strong market presence and a dedicated customer base can lose their value if this data isn’t analyzed efficiently.

In this blog, we’ll unravel How to Build an AI Model and how they can identify trends, patterns, and anomalies to predict demand and customer preferences by sorting mountains of data.

What is an AI Model?

AI is short for Artificial Intelligence, which can become a great companion for businesses looking to enhance their productivity by making better decisions. With data analysts, scientists, and developers on board, several AI models have been built that can predict outcomes for any industry.

An AI model is a trained computer program that can autonomously perform specific tasks and predict decisions rather than simulate human intelligence. Developers feed high-quality historical data to train it to recognize patterns, trends, and relationships in any situation and make accurate and relevant decisions.

For example, banks can build AI models to predict fraud by analyzing consumer behavior data and specific activities that lead to fraudulent transactions. These models can then predict the occurrence of fraud when such instances happen again.

Some advanced techniques, such as Deep Learning and Neural Networks, allow AI models to make future predictions. Some complex models also use ensemble learning with techniques like boosting, bagging, and stacking.

Types of AI Models

By automating rule-based tasks, owners can focus on strategic planning tasks and optimize resources with quick response times. Two broad categories of AI models can be developed for new product development and growth opportunities:

Based on the Learning Type

Building an AI model based on the learning type is an approach in which a machine learns by itself and doesn’t work on any defined relationships or patterns. Developers feed randomized data to the machine to let it identify trends and patterns on its own.:

Supervised Learning Models

Supervised learning uses labeled training datasets for algorithmic training to classify data by learning the relationship between a given input and desired output. To get accurate results, the algorithm measures the loss function and adjusts the errors accordingly to minimize them.

Unsupervised Learning Models

An unsupervised learning model identifies patterns without any human input. These models work on self-learning algorithms that receive raw data to create their own rules. For example, if the model is provided with a dataset of types of birds, it will group them based on their sounds and locations on its own.

Semi-supervised Learning Models

This learning model combines supervised and unsupervised learning and is trained on small labeled datasets and large unlabeled datasets. The semi-supervised model can identify data clusters under the guidance of unsupervised learning and use supervised learning to label them.

For example, deep learning tools like Generative Adversarial Networks (GANs) train two neural networks to generate unlabeled data.

Reinforcement Learning Models

The Reinforcement Learning from Human Feedback (RLHF) model is a dynamic learning model that uses algorithms based on reward and punishment. An agent learns optimal strategies through actions and feedback, receiving rewards or penalties based on performance metrics. Common use cases of RLHF are video game development and teaching robots to replicate human tasks.

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Based on Task Type

This domain of AI models can be divided into formal, expert, and routine tasks. Routine tasks require more knowledge, complex representations, and algorithms. Expert tasks need expert knowledge but no common sense. Similarly, formal tasks require applying formal logic and some learning.

Classification Models

Classification models can predict categorized output variables, e.g., “spam” and “not spam.” They label input data pieces using algorithms like logistic regression, Support Vector Machines (SVMs), and K-nearest neighbors (KNN).

Regression Models

Regression models predict output values by identifying linear relationships. Algorithms like linear regression and random forest can establish relationships between real or continuous variables like age, income, temperature, gender, etc.

Clustering Models

Clustering models group data points into different clusters according to their similarities. This model is extremely useful for identifying patterns in data without needing labeled examples. Popular algorithms include Mean-shift, Principal Component Analysis (PCA), and K-means clustering.

Dimensionality Reduction Models

The dimensionality reduction method represents a dataset by removing irrelevant features while still maintaining the meaning of its original dataset. Using data compression, it extracts and combines the features to build a model with techniques like PCA and Linear Discriminant Analysis (LDA).

Use cases include Latent Semantic Analysis (LSA) through Natural Language Processing (NLP).

Generative Models

Generative models work on inputs like codes, texts, images, videos, and audio to generate new content. It can turn text inputs into image outputs or videos into texts. The most popular generative models today are ChatGPT, Google Cloud AI, and Amazon Alexa.

Sequence Models

Sequence models are a class of ML designed for tasks involving sequential data. In these models, the order of the elements is important when providing input. Examples include texts, audio signals, video streams, and time series. Some use cases include speech recognition and sentiment analysis.

Recommender Systems

Recommender systems are a type of AI algorithm that uses Big Data to suggest and recommend products. Based on historical data such as purchase & search history and demographics, they provide insights to users. Some popular examples are Netflix and Spotify.

Conceptual Layers of AI Model

The AI model architecture needs to consider the model’s value propositions and the right method. The three most common options are no-code/low-code platforms, traditional programming and ML libraries, and AutoML.

We suggest choosing the AutoML method, which can provide a middle ground and is both convenient and customizable. ChatGPT and Google’s Bard are some of the most popular examples of building generative models with AutoML.

Let’s understand the conceptual layers:


The infrastructure layer defines the computing power needed for an AI model to process and analyze data. AI’s infrastructure is further divided into components like computing infrastructure, networking infrastructure, storage infrastructure, and platform/application layers.

Hence, it’s built on all the hardware components, like Graphic Processing Units (GPUs) and servers, to speed up the AI computations. Some recommended tech stacks are PyTorch and GPU toolkits like CUDA.


An AI model’s entire existence depends on data, so the data layer forms its bedrock. It provides infrastructure for the storage, management, and processing of data for analytics. The main tasks under this layer are data cleaning, transforming, standardizing, and enhancing.

The main components of this layer are data storage, databases, data warehouses, and lakes, as well as ingestion, processing, and governance. This is also the layer where enterprises can benefit from using AI and ML together for the best results.


This is the layer where AI models begin their inception. By choosing relevant algorithms, developers create and train the model. Similarly, they further tune the hyperparameters and design neural network layouts to train the model on labeled data.

For example, BERT, GPT-3, and ResNet are some of the pre-trained models that developers can utilize to save time and resources while creating new applications.


The service layer enters just before when an AI model is ready to be deployed. It is a delivery system that enables the deployment and management of AI models in the real world.

It also involves creating Application Programming Interfaces (APIs) to integrate existing systems and establish communication. Containers and microservices architecture are commonly deployed to quicken the deployment, scaling, and monitoring of the model.


The application layer decides how to integrate its capabilities with business apps and procedures. Hence, it includes building apps based on predictions and suggestions made by AI models and using the insights to make decisions. Common use cases for these applications include supply chain management, fraud detection, and customer services.


For successful integration of enterprise applications with the AI model, large language model (LLM) orchestration serves as the foundation for the integration layer. To add more value, this layer consists of executing advanced strategies and maintaining stateful conversations.

This layer also defines the security levels. Hence, it’s crucial to choose an AI development company that can easily switch between dynamic resource allocation, version control, and real-time monitoring.

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6 Steps to Build a Custom AI Model

An AI system requires some key components to build a strong backbone for your model:

  1. High-quality data
  2. Clearly defined algorithms strong enough to digest the data (decision trees and deep learning networks)
  3. A robust infrastructure that can train and deploy an AI solution (on-premises or cloud)

Integrating AI into systems should be a balancing act of avoiding over and underfitting structures. To ensure your AI development goes smoothly, here are some steps you can take:

Fundamentals and Preparation

Building any new product requires answering the simple question of what problem it aims to solve. Before you start fetching details, make sure you have relevant data for AI to solve your specific problem. Moreover, it should also benefit the organization.

Expert Tip: Set measurable targets that the AI model can achieve. For example, detecting and preventing 15% to 20% of cyber attacks in a month.

Data Handling

Since AI models work on trained data, the datasets must be fetched from high-quality internal databases, properly handled, and cleaned. Data handling tools like Python’s Pandas library are extremely efficient at removing missing entries.

Expert Tip: Our advice is to use AI based web development tools like Scikit-learn for easy data analysis and modeling. It’s a brilliant option for ML tasks like regression and classification and even more so for web applications.

Model Selection and Setup

Java and Python remain the top programming languages for AI model selection as they provide a robust set of ML libraries. Other languages, like R and C++, are excellent for data science and gaming AI, respectively. For model setup, you can choose from AWS or Google Cloud for scalability or local servers, depending on your needs.

Expert Tip: We recommend choosing ML platforms and tools like Amazon SageMaker for fully managed workflows & infrastructure and Keras for quick prototyping and data processing.

Training and Optimizing the Model

Once data cleanup is processed and gathered according to the problem defined, the next step is to train the model by applying some techniques and algorithms. This involves setting hyperparameters, training and validation, model development and testing, and optimization.

Expert Tip: Linear Regression is a highly popular method for mapping correlations. TensorFlow and PyTorch are two recommended tools for building pre-trained models that can be fine-tuned as per needs.

Evaluation and Refinement

There are multiple downstream models focused on specific tasks, and an upstream model is used to delineate the model splits. Hence, the evaluation stage helps assess the AI model’s performance with the set goals. Some of such techniques are confusion matrices, precision-recall, and Receiver Operating Characteristic (ROC) curves.

Expert Tip: To measure accuracy, ensure that the model performs equally well on trained and unseen data. Moreover, ensure it isn’t biased towards specific outcomes due to data skewness.

Deployment, Maintenance, and Ethical Considerations

The last step is to operationalize the model by deploying it and developing a benchmark to measure future iterations. You can either integrate it into existing systems with APIs for system interoperability or develop an interface for user interactions.

Expert Tip: Tools like Docker and Kubernetes are recommended for managing cloud deployments. TensorFlow’s TensorBoard and Google’s Cloud Monitoring are also useful for monitoring and maintenance.

Finally, How to Build an AI Model?

So, Building an AI model involves defining the problem, gathering and preparing data, selecting and training the appropriate algorithm, and evaluating its performance. Key steps include data cleaning, feature engineering, and hyperparameter tuning to optimize the model for accuracy and reliability.

Build Your Custom AI Model With Moon Technolabs

Building your first AI model opens new opportunities and provides valuable insights, shaping the future collaboratively with AI rather than being replaced by it. At Moon Technolabs, we understand that setting up a proper roadmap for AI model development makes the entire process seamless.

However, writing and training algorithms are complex tasks that demand a data science expert or a team of data scientists. Hire Our AI Development Team, which focuses on addressing user pain points and effortlessly resolving them with innovative AI solutions. Contact us today to start your AI journey and drive growth and efficiency in your operations.


A few decades ago, the idea of machines mimicking human intelligence seemed far-fetched. Today, we’re amid a digital revolution where AI models are crucial for enterprises, enhancing efficiency and accuracy across industries. AI has simplified complex tasks, from financial predictions to healthcare diagnostics, demonstrating its limitless applications.



How are AI models built?

Building AI models is not as complex as it may seem. With the right data and tools, you can start building your AI model. Whether you choose traditional programming, auto machine learning (ML), or no-code/low-code platforms, the process is within your reach. While we are currently limited to building Artificial Narrow Intelligence (ANI) or Weak AI, the potential for Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI) is an exciting prospect.


How do we build a generative AI model?

Building a generative AI model is a learning process similar to teaching a human how to recognize images. The model is trained with similar examples, gradually becoming intelligent enough to create something entirely new from the data it's fed. This involves ML, language models, and neural networks, all of which enhance computers' ability to understand word sequences.


How do you build an AI model in Python?

Building an AI model in Python requires a neural network to generate output from inputs using NumPy. Neural networks are the key components of AI models, as they take input data, make predictions, and compare predictions with desired outcomes to adjust the internal structure for further correct predictions. You can contact an AI development expert who will help you build your next AI model.
About Author

Jayanti Katariya is the CEO of Moon Technolabs, a fast-growing IT solutions provider, with 18+ years of experience in the industry. Passionate about developing creative apps from a young age, he pursued an engineering degree to further this interest. Under his leadership, Moon Technolabs has helped numerous brands establish their online presence and he has also launched an invoicing software that assists businesses to streamline their financial operations.