A Step-by-Step Guide to Creating an AI Model

The buzz around artificial intelligence is hard to ignore, and it is a great opportunity to sharpen your skills and enhance your app with a custom AI model. 

While it offers endless opportunities, like automating mundane tasks, the journey can seem daunting. Where do you start? What tools do you need? And how can you bring your ideas to life in a field that often feels reserved for the tech elite?

The good news is that you don’t need to be a data science wizard to embark on this adventure. We will guide you step-by-step, breaking down complex concepts into manageable chunks, and providing you with the practical knowledge you need to start building an AI model.

AI Fundamentals

Let’s start with the basics. This knowledge will help you navigate the field and quickly grasp the essentials.

An AI model is an algorithm that learns from data to perform tasks that typically require human intelligence, such as recognizing patterns, making decisions, or predicting outcomes. It is trained using data to identify relationships and insights that can be applied to a new batch of data.

AI and Machine Learning

Artificial Intelligence is the simulation of human intelligence by computer systems. This encompasses various technologies and approaches, including reasoning, learning, problem-solving, perception, and language understanding.

Types of AI:

• Artificial Narrow Intelligence (ANI): it is designed to perform a specific task very well, such as facial recognition or language translation. Most of the AI we see today falls into this category.
• Artificial General Intelligence (AGI): an AI system that can understand, learn, and apply knowledge across various tasks, like a human. AGI remains a goal rather than a reality.
• Artificial Superintelligence (ASI): this hypothetical system surpasses human intelligence and capabilities in every aspect. ASI is an even more farther goal, than AGI. Still, Sam Altman, the creator of OpenAI, thinks that we have a few thousand days left until this happens.

Machine Learning (ML) is a subset of Artificial Intelligence focused on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.

Instead of being explicitly programmed for every task, ML algorithms improve their performance as they are exposed to more data over time.

Also, there is a difference between AI and neural networks. Neural networks or NN are a specific type of Artificial Intelligence inspired by the structure and function of the human brain, that can train and adapt. The field is not limited to just NN and includes other techniques such as reinforcement learning, genetic algorithms, expert systems, etc.

NN architecture consists of interconnected layers, where each layer transforms the input data through weights, biases, and activation functions to produce an output. This structure enables the network to learn from data and make predictions.

Deep learning is a subset of machine learning that uses neural networks with many layers (hence “deep”) to analyze large amounts of data. Each layer learns to recognize different features. For example, in image processing, the first layer might detect edges, the next layer could identify shapes, and so on.

Unlike traditional machine learning, this type can automatically discover the features needed for classification or prediction. So you don’t have to select and engineer these features manually.

AI technologies to leverage in apps

Various technologies enable machines to perform tasks that typically require human intelligence. Here are key examples you can use in creating an AI model:

• NLP: Natural language processing allows computers to understand, interpret, and generate human language. It connects human communication with computer understanding. Examples include chatbots, language translation, sentiment analysis, and voice assistants like Siri or Alexa.
• Computer vision: helps interpret and make decisions based on visual data from the world, such as images and videos. This technology is used in facial recognition, object detection, image segmentation, and even in medical imaging for diagnosing diseases.
• Speech recognition: computers can transcribe and understand spoken language. The technology converts spoken words into text format. It is used in virtual assistants, automated transcription services, and voice-activated controls.

Types of AI Models: supervised vs unsupervised learning

There are various criteria, such as their approach to learning, the type of task, and the kind of data they utilize.

Here are some common types:

Supervised learning models 

These models learn from labeled data, meaning the training data set has both inputs and expected outputs. So the model learns over time. For example:

• Linear regression
• Decision trees
• Support vector machines (SVM)
• Neural networks

Unsupervised learning models  

They work with unlabeled data, attempting to find hidden patterns or intrinsic structures in the data. Examples include:

• K-means clustering
• Hierarchical clustering
• Principal component analysis (PCA)

Semi-supervised learning models  

Semi-supervised models combine a small amount of labeled data with a large amount of unlabeled data. They are useful when obtaining a full set of labeled data is expensive or time-consuming.

Reinforcement learning models  

These AI systems learn by interacting with an environment and receiving feedback in the form of rewards or penalties. This model type is commonly used in robotics and game-playing scenarios.

Deep learning models  

A subset of machine learning that utilizes neural networks with many layers. Deep learning models are particularly effective for tasks like image and speech recognition. For instance, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).

Generative models  

These models learn to generate new data points that mimic the training data distribution. The category includes Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

How to build an AI model

The process involves several systematic steps. Following them provides a solid framework, however, remember that the steps vary depending on the application and technology used.

Here’s a simplified guide to help you through the process:

Define the problem  

 First, understand what you want to achieve with AI algorithms. Consider questions like:

• What specific task or challenge do you want the AI to address?
• What outcomes do you expect?

Don’t forget to get insights from stakeholders, learn their needs, and discuss requirements and perspectives. Assess which aspects of the problem are the most critical. This helps in narrowing down the focus and resources appropriately.

Then, familiarize yourself with the domain where you want to apply Artificial Intelligence by researching existing solutions and analyzing current pain points. You also need to clearly outline how you will measure success. Set specific KPIs or determine acceptable performance levels for the model.

Finally, write a comprehensive problem statement including the essence of the issue you are addressing, like:

• The symptoms of the problem.
• The impact of the problem on the end users or the business.
• Any constraints or limitations that must be considered.

Gather data

Collecting the right data is essential for successful AI development. First, determine what type of data you need based on the problem–think about data structure and relevant features. Once decided, collect relevant and sufficient data from various sources, such as databases, web scraping, or public datasets.

If existing data is insufficient, conduct surveys to collect quantitative data and interviews for qualitative insights. What’s more, you can collaborate with domain experts to get valuable data sources and gain insights into data collection methods.

Preprocess data  

As you gather data, prioritize quality over quantity. This involves validation and cleaning to remove inaccuracies. For instance, handle missing values (e.g., using imputation or removing records) and remove duplicates or irrelevant records.

Convert your data into a suitable format: normalize or standardize numerical features to ensure they are on a similar scale. Leverage techniques like one-hot encoding or label encoding to encode categorical variables.

 For text data, consider:

• Tokenization to split the text into words or phrases.
• Removing stop words, punctuation, and special characters.
• Stemming or lemmatization to reduce words to their base forms.

If you notice any class imbalances, you can try oversampling minority classes, undersampling majority classes, and using techniques like SMOTE (Synthetic Minority Over-sampling Technique).

Choose the right tools

When creating an AI model, there are many tools you can leverage: programming languages, frameworks, etc. Let’s take a look at the most popular solutions for beginners:

Programming languages

Python is widely used in developing Artificial Intelligence due to its ease of learning and a large ecosystem. It is an excellent choice for machine learning because of production-ready flexibility. The language offers many popular libraries and frameworks like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, etc.

R was developed as a statistical language, so it’s a perfect option for statistical analysis and data visualization. The libraries include caret, randomForest, and nnet.

Java is less popular in the AI field than Python, however, it’s still a viable solution. It offers features like simple debugging and cross-platform development. Java is also robust, secure, and easy to maintain—great qualities for enterprise applications. As for libraries, take a look at Weka, Deeplearning4j, or MOA.

Frameworks

TensorFlow is an open-source tool developed by Google for creating deep learning and machine learning models. It offers a flexible architecture with great scalability potential. 

PyTorch is a machine learning framework developed by Facebook. Written in Python, it has a low learning curve for most developers. It provides great flexibility, a dynamic computation graph, and a user-friendly interface for creating an AI model.

Keras High-level API running on top of TensorFlow, making it easier for beginners to build and train deep learning models. You can leverage a high-level interface for developing and training deep learning models. Its main goal is to make the process user-friendly for both novices and specialists.

Split data

Divide your data into training, validation, and testing sets. Typically, the training set is used to train the model, the validation set to tune parameters, and the test set to evaluate performance.

An example of splitting is:

• 70% training
• 15% validation
• 15% test

Train the model  

Feed your training data into the chosen model and allow it to learn patterns. This involves adjusting the model parameters based on the input data.

For each batch, perform the  following:

• Forward pass: Calculate the predicted output.
• Compute the loss using the loss function.
• Backward pass: Implement backpropagation to update weights based on the loss.
• Repeat this process for a set number of epochs (complete passes through the entire training dataset).

Validate the model  

Use the validation set to test the model’s performance. Adjust hyperparameters like learning rate, batch size, and number of layers to improve accuracy and avoid overfitting. You can also leverage techniques like grid search or random search.

Test the model  

Evaluate the final model’s performance using the test dataset. Measure metrics like accuracy, precision, recall, and F1-score to assess how well the model performs.

Optionally, use k-fold cross-validation to assess the model’s performance more robustly. This involves splitting the dataset into k subsets and training/testing k times while rotating the training and test sets. During AI development, keep comprehensive records of the testing process, outcomes, and any insights gained. To help stakeholders understand the model’s performance, report to them in further iterations.

Deploy the model  

Once satisfactory performance is achieved, deploy the model to a production environment. This could involve integrating it with a web app, API, or other systems. Since the AI system works with data, ensure security with authentication and authorization measures for your API. Additionally, you can consider data encryption.

After deployment, run tests in the production environment to check that everything works as expected. Include load tests to validate the model’s performance under various conditions.

Monitor and maintain  

Continuously monitor the model’s performance and gather feedback. Based on new data or changing requirements, update and retrain your model as necessary.

Keep track of different versions of your model to ensure backward compatibility and facilitate updates.

How much does it cost to build an AI model

The expenses vary greatly. Small-scale projects may range from a few thousand dollars, while larger, more complex projects can reach hundreds of thousands, depending on the scale and requirements. You need to define your goals clearly to estimate costs effectively. 

Here’s a breakdown of what can influence the overall cost:

• Data acquisition: Gathering high-quality data can be one of the most significant costs. If you need to purchase datasets, this can be expensive. Additionally, data cleaning and preprocessing also require money.
• Infrastructure: The hardware and cloud services used for model training affect costs. Cloud platforms often charge based on usage, so budget for computing power, storage, and other resources.
• Development time: The time spent on developing your model contributes to costs. This includes planning, data gathering, model training, and testing. Hiring data scientists or engineers significantly increases expenses. So the longer you develop, the more expensive it gets.
• Model complexity: Complex AI algorithms, like deep learning networks, generally require more computational resources and time to build, thereby increasing costs.
• Software and tools: Licenses for software and tools (like machine learning frameworks and data processing libraries) add to the overall expenses. However, many tools are open-source.
• Maintenance and updates: Continuous monitoring, maintenance, and retraining of the model are costly. Still, keeping the model up to date with new data is essential.
• Consultancy and services: If you seek external expertise, hiring consultants or partnering with organizations specializing in AI requires money.

Conclusion

Embarking on the journey of building an AI model can be both exciting and daunting. Start small, experiment with pre-built frameworks, and gradually work your way up to more complex projects. Remember, learning is an iterative process, and every setback is an opportunity to refine your skills. 

Do you want to create an AI-powered app? Reach out to us, and let’s build a project that automates routine tasks and streamlines processes.