How to Build an AI Model: Challenges, Tools & A Development Guide

AI model development is overwhelming for most developers (you’re definitely not the only one). From ideation to architecture selection and data preparation, the challenges add up quickly. That’s why it’s necessary to have a clear strategy for how to build an AI model before beginning.

Without proper knowledge and the right tech stack, you may struggle to bridge the gap between ideation and execution. This guide provides an overview of how to create and fine-tune AI models effectively. We also explain how to choose the right tools and overcome key challenges to build a model that delivers measurable value.

Get expert guidance on building your custom AI model

Schedule your free technical consultation

What is an AI Model?

An AI model is a trained computer program that analyzes data, recognizes patterns, and/or makes autonomous decisions. These models might use machine learning (ML) and deep learning to become more accurate for certain objectives.

The functionality of AI models varies depending on the task for which they’re built and the data they’re fed.

Why do businesses implement AI models?

AI models are strategic innovations that boost operational efficiency and may yield a high return on investment. Let’s look at the practical value they can bring to all sorts of enterprises:

• Efficiency through automation. Robotic process automation tools, like AI-powered virtual assistants, handle numerous repetitive yet labor-intensive tasks, from updating CRM systems to extracting valuable information from notes.
  
• Real-time decision-making. AI-powered platforms can assess customer demand, identify market trends, analyze customer segments, and use insights from that information for process optimization and price adjustments.
  
• Personalized customer experiences. E-commerce and retail companies use AI to analyze their customers and deliver targeted recommendations and marketing campaigns that resonate.
  
• Advanced cybersecurity measures. AI can continuously monitor network activity and user behavior to verify login attempts, identify potential fraud, and detect malicious attacks.
  
• Content generation. Generative AI can create original text and graphics with multiple design variations (for products, manufacturing equipment, building architecture, and so on).
  
• Predictive analytics. Sophisticated machine learning models analyze historical data to forecast future outcomes. For example, they can predict churn rates and inventory needs, or enable proactive equipment maintenance.

Five Layers Essential to Building an AI Model

Companies can take existing foundations of a model and retrain them for a specific task or a niche demographic. To do this, it’s important to understand the layers of these models.

1. Infrastructure layer: Provides the foundational hardware and computing resources to support AI model workloads.
   
2. Data processing layer: Manages data collection, storage, insight extraction and transformation; gathers data from various sources, including IoT devices, customer interactions with your company, and enterprise systems.
   
3. Service layer: Ensures that AI models can interact with other software applications and systems; usually communicates with the help of APIs.
   
4. Model layer: The core of the AI architecture, where developers create models trained to perform specific tasks.
   
5. Application layer: Allows interaction with the AI model via user interfaces and dashboards.

Depending on your project’s scope, you might not need to create an AI from scratch — you can just build upon an existing AI foundation. However, model development should always follow a plan.

How to create an AI model: A step-by-step guide

To build a reliable AI model, teams should follow a strict process for development, whether using traditional methods, specialized low-code methods, or automation tools.

1. Problem identification and planning

An AI model must solve a real problem, but the problem is not always obvious. It’s essential that your model finds a genuine use case and a target audience.

• Take extra time to research pain points. The problem you solve can be operational (process automation), predictive (accurate forecasting), or analytical (identifying behavior patterns and segmenting customers).
  
• Establish measurable success metrics. Use specific, measurable, achievable, relevant, and time-oriented goals (SMART) to ensure success. Prioritize quantifiable targets, such as reducing data breach detection time by 10%.
  
• Identify the type of AI you need. This includes the algorithm foundations, necessary data for training (structured spreadsheets; unstructured images or text), and infrastructure requirements.
  
• Consider your budget, resources, and time constraints. Determine whether you have the necessary tools, frameworks, and talent in place to build your model on time and within your budget.
  

In case of skill gaps, you may need to augment your team or hire a dedicated team.

2. Selecting an architecture model

In machine learning, architecture means the structure of algorithms that determine how the model processes data, extracts features, and makes predictions. Each architecture consists of layers of nodes or neurons to accomplish specific goals.

• Sorting and clustering algorithms identify patterns and group data with similar data points. Uses include segmenting customers and detecting anomalies in transactions.
  
• Convolutional neural networks (CNNs) specialize in processing visual data. They can detect and classify objects in images. For example, in retail, CNNs might recommend items for customers to purchase based on visual similarity to items they’ve viewed or purchased before.
  
• Recurrent neural networks (RNNs), including long short-term memory and transformers, handle sequential data and predictions. Organizations use RNNs for stock forecasts, customer churn modeling, and the like.
  
• Generative models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), learn to produce new content based on training data. Examples include image and text generation platforms, like MidJourney and OpenAI.
  

After selecting the architecture for your model, you can proceed to source and prepare training datasets.

3. Collecting, cleaning, and preparing data

To successfully create an AI model, you must train it on accurate, relevant, and diverse data. Here are a few tips for how to properly collect and prepare training datasets.

• Maintain a repository for training data. To improve the reproducibility and transparency of results, all data should be stored in a central repository that has version control.
  
• Ensure datasets are correctly labeled. Distinguish between content types (topics, demographics, sectors, or coding languages).
  
• Use metadata to tag datasets. Tagging helps you trace data used for training, which will be helpful during audits and troubleshooting.
  
• Clean your datasets. Remove duplicate and erroneous data, copyrighted materials, and personally identifiable information (PII).
  

You should split your data into several datasets for training, validation (fine-tuning), and testing (performance evaluation). Splitting datasets also prevents model performance from worsening when new (production) data is added to the training data (a phenomenon called overfitting).

4. Training, validating, and hyperparameter tuning

During training, AI models learn to identify relationships in the data through multiple iterations, during which they map inputs (features) to correct outputs (predictions).

After generating a prediction, the model compares it with the actual output, and the difference is measured as a loss function. Since you can’t build a perfect AI model, you should expect some prediction inaccuracies. However, several strategies can minimize the loss function and improve reliability.

• Introduce backpropagation to calculate how much each model weight contributed to the error in specific features.
  
• Use optimization algorithms, like stochastic gradient descent (SGD) and adaptive movement estimation (Adam), during testing iterations to adjust weights and, subsequently, correct errors.
  
• Fine-tune hyperparameters manually by experimenting with sample sizes, learning rates, and regularization.
  
• Optimize with multiple graphics processing units (GPUs) to process several large datasets in parallel, which can accelerate the building of an AI model.
  
• Test on unseen data to make sure the model can adapt to new scenarios instead of memorizing existing features.
  

Companies can use configurable pre-trained models. For example, e-commerce companies can fine-tune a pre-trained recognition model to identify feedback about their products on social media.

5. Application development

When developing an AI application, consider how scalability, interoperability, and user experience will impact your trained AI model. Make sure it will operate within your business’s infrastructure. To do this, use the previously defined success criteria. A few things to consider:

• Implement modular architecture. The modular architecture breaks down the AI solution into isolated and independently managed components (microservices). This lets you update and fine-tune different functionalities, like the recommendation engine and automation modules, without impacting the entire model.
  
• Choose an infrastructure based on your needs. Cloud-based platforms with autoscaling work best for resource-intensive models, while on-premises infrastructures have better isolation.
  
• Make the experience convenient for your audience. Ensure you understand your target user’s technical background and skills when designing the user interface (UI). A command line interface may be sufficient for data scientists and engineers, but supply chain managers may need a proper graphical user interface (GUI) with interactive dashboards.
  

You‘ll need in-depth monitoring and may need several attempts to properly deploy the model and ensure correct performance.

6. Continuous monitoring and improvement

AI models require continuous performance monitoring (with tools like TensorBoard) and output checks to ensure they operate efficiently, learn correctly from new data, and don’t degrade with time. Here are the things to look out for:

• Concept drifts. Monitor the model’s output for data drift and concept drift, which is when the relationship between inputs and outputs changes.
  
• Bias checks. Evaluate the model regularly for unintended bias to ensure fairness and ethics in decision-making.
  
• Performance indicators. Track accuracy, precision, and latency metrics to ensure the model meets your business expectations and service-level agreements.
  
• User feedback. Active users will be your main testers throughout the lifespan of your AI model. Build an intuitive communication platform to receive to complaints, concerns, and suggestions.

As you collect new data, you should continuously retrain and optimize the model to maintain precision. Luckily, supporting tools can smooth the entire process.

Tools and Technologies To Help You Create AI Models

To facilitate AI model building, your strategy should include the right combination of technologies and development tools. There are many options, so we’ll just name a few that can speed up production and improve model training.

• Low-code and no-code platforms: Offer visual tools, prebuilt connectors, and templates that allow building AI models with minimal code-writing. Examples include platforms like Pecan AI.
  
• Programming frameworks: Programming frameworks like TensorFlow, PyTorch, and Keras can help build, train, and optimize highly specific models.
  
• Automated machine learning tools: These tools help automate aspects of model-building, such as feature selection, hyperparameter tuning, and model evaluation, such as Google AutoML and H2O.ai.
  
• Foundational models: These AI models are already trained on large datasets and offer several reusable frameworks to speed up development.
  
• Monitoring software: Use monitoring tools like Google Cloud Monitoring, TensorBoard, and TensorFlow to track your model’s performance.
  

In addition to these supporting technologies, your team should have proper experience with AI development in order to avoid challenges that can arise during production and post-deployment.

Key Challenges of Creating AI Models

Building AI models involves several challenges, ranging from data quality to security risks. To avoid unnecessary costs and problems, you should know how to make an AI model while avoiding these problems.

• Privacy and compliance. AI often requires sensitive user data protected by privacy laws such as GDPR and HIPAA. To comply, you must get user consent to collect and use someone’s data. Implement data anonymization tools that strip PII from datasets. You should also conduct regular compliance audits to ensure that you’re handling data properly.
  
• Ethical practices. Businesses need to understand how to make an AI model that is explainable and ensures fairness. In addition, responsible AI and machine learning practices require ethical frameworks (like in Microsoft’s checklist) and strict rules for data preparation.
  
• Data security. Breaches and data misuse compromise personal information, causing companies reputational and financial damage. To properly protect your sensitive information, implement advanced encryption, access controls, multi-factor authentication mechanisms, and anonymization tools.
  
• Interoperability issues. AI models can be difficult to integrate into existing workflows and systems, which can cause data transfer errors and duplicates. It helps to prioritize the development of APIs to ensure that your model works with your current tools. You might also need to refactor your legacy applications and systems into a modular architecture.

Create an Accurate, Fair, and Scalable AI Model

AI models need ongoing refinement for continuous learning. It’s not just about the initial successful deployment that counts, but also ensuring the model delivers sustained value.

Choosing the right tools and frameworks, managing scalable infrastructure, and maintaining compliance with regulations are critical for long-term success. Sometimes, you may need help to cover some aspects of AI development.

At Techstack, we develop scalable AI models and sophisticated algorithms with advanced technologies like computer vision, natural language processing, and deep learning. If you’re interested, you can explore our portfolio or reach out directly.