How to Choose The Right AI Model For Your Application?

How to choose the right AI model for your app in 2024?

An Artificial Intelligence (AI) model is an algorithm that recognizes specific patterns or makes certain decisions based on the data it receives. It is trained to work without human intervention and learns from its experience. The business opportunities AI models provide within those key features are almost endless. Choosing the right AI models for apps is crucial for your company's growth and adjustment to changes.

It all began in the early 1950s with a computer game. Leading scientists were training machines to play chess with humans. To win, programs needed to stop following a prescripted sequence of moves and start making moves in direct response to their opponents. Those chess-playing programs evolved since then dramatically. They became tools, able to analyze vast amounts of data, recognize patterns, and make predictions.

Nowadays, the power of AI works in healthcare, automobile, retail, and other sectors that value innovations. If you want to modernize an existing app or build a new “smart” one and look for a proper AI model for your application, this article is for you. It explains what AI models are, their types, and how they work. Here, you will find tips on how to choose the suitable AI model for your next app.

Considerations Before Choosing an AI Model

The Artificial intelligence world is growing fast. Each AI model has its unique requirements for a training dataset and parameters. The wrong choice endangers output accuracy and can negatively impact your project. This guide will walk you through possible challenges with a four-step approach.

Define Your Use Case

What are your enterprise's specific AI demands? What are the problems your custom application aims to solve? For instance, you might want to automate customer support by reducing response time and improving customer satisfaction. Once defined, it becomes easier to determine the scope and complexity of your AI requirements and align them with your business goals.

Data Availability and Quality

AI models rely on data much like an engine depends on fuel. You should ensure its quality, availability, and relevance. Pay attention to data volume, variety, and cleanliness. If there are any gaps, inconsistencies, or quality issues, it's crucial to address them before AI implementation.

Real-Time vs. Batch Processing

Are you planning to build a chatbot or voice assistant? If so, providing a smooth user experience is critical. With real-time processing, your AI model generates responses with minimal latency immediately as user inputs are received, ensuring a seamless experience. For less time-sensitive tasks, batch processing allows for handling larger data volumes efficiently while operating on a delayed, scheduled basis.

Performance Requirements

An AI model should balance speed, accuracy, and resource utilization to meet business needs. Legal document analysis or medical applications require high accuracy, meaning the model must prioritize precision, sometimes at the cost of speed or computational efficiency. On the contrary, models for customer support systems must generate quick responses. It takes higher priority, and some loss of accuracy is acceptable.

Once you decide what application to build next, consider the mentioned steps to avoid challenges and get ready to choose the right AI model for your application.

Common Types of AI Models

We use words like artificial intelligence and machine or deep learning interchangeably in everyday speech. When running a business, though, a clear understanding of the basic concepts of AI is vital. The following few paragraphs will help you navigate through them.

Machine Learning (ML) Models

Machine learning is a well-known subset of artificial intelligence, powering many digital products and services we use daily. As Coursera experts explain, ML uses algorithms to create self-learning models capable of predicting outcomes and classifying information. The point is to enable machines to handle tasks that previously only humans could do—making strategic moves in chess, classifying images, or predicting housing prices.

An algorithm in ML is essentially a set of mathematical instructions or procedures that learns patterns from past data. When it receives new data, an algorithm can make predictions or categorize information based on those patterns. Training it, though, may require multiple iterations and time. Once trained and tuned, an algorithm becomes a machine learning model that can be used to make predictions on new data.

Deep Learning Models

Algorithms arranged in multiple layers to create complex networks are known as deep learning models. These models may use hundreds or thousands of layers to perform increasingly complex and nuanced tasks. Self-driving cars, digital assistants, voice-activated TV remotes, and generative AI are all examples of deep learning applications.

The power of deep learning models comes from their ability to perform unsupervised learning. This allows them to work with raw and unstructured data, extracting characteristics, features, and relationships to generate accurate outputs. Unlike many traditional ML algorithms, which often require labeled data to “learn,” deep learning models can uncover patterns in vast datasets independently, enabling more flexible and scalable solutions.

Natural Language Processing (NLP) Models

Natural Language Processing (NLP) transforms how machines understand, process, and generate human language. It draws on principles of linguistics, computer science, artificial intelligence, and cognitive psychology to bridge the gap between human communication and computer interpretation.

Applications of NLP are reshaping industries worldwide, from language translation tools to chatbots that provide customer support. In healthcare, NLP is used to examine medical texts to extract valuable insights from clinical notes. This helps improve diagnostics and supports research efforts. However, researchers warn about the challenges of NLP, such as bias mitigation, contextual understanding, and ethical considerations.

Pretrained vs. Custom Models

Models trained on large datasets for general tasks are called “pre-trained.” Examples include models like GPT and BERT, which can be adapted to specific tasks with fine-tuning.

On the other hand, custom models are built and trained from scratch or heavily modified for a specific use case. They offer more flexibility and can be created to fit a project's unique requirements but require more data and computational power.

How to Evaluate AI Models for Your Application

Now that you're aware of existing AI Models for Apps and their differences, you're ready to decide which one will be the most effective for your use case. Those four insights will show how well the model performs under different conditions.

Accuracy, Precision, and Recall

How often does a model make correct predictions? It is evaluated by its accuracy, precision, and recall. In a classification task, a model’s accuracy measures the proportion of all correct predictions. Precision measures the proportion of true positives among all positive predictions. And recall represents the proportion of all positive cases the model correctly identifies as positive.

Try not to rely solely on one metric, as it may lead to misleading conclusions. For instance, a model with high accuracy but low precision may correctly classify most instances but also generate many false positives. It can be problematic in tasks like spam email detection. Important legitimate emails may end up in the spam folder, resulting in a poor user experience. A high accuracy and low recall combination is poorly suitable for the diagnosis of rare diseases as the cost of false negatives is high.

To improve evaluation, it is worthwhile to use techniques like confusion matrices and F1 scores, which combine precision and recall for a balanced view.

Overfitting and Generalizability

Overfitting occurs when a model performs exceptionally well on training data but fails to generalize to new, unseen data. This happens when the model learns the noise and specific patterns in the training data rather than the underlying trends. Regularization techniques, cross-validation, and ensuring the dataset includes diverse examples can mitigate overfitting.

Generalizability, on the other hand, refers to the model's ability to perform well on various datasets. Models with better generalizability are more valuable because they can be applied to different environments with minimal performance degradation.

Scalability and Efficiency

Can the model handle growing data volumes and increased computational demands without a significant loss in performance? That’s scalability. How quickly can the model process inputs and generate outputs? That’s efficiency.

An AI model for real-time applications must be accurate and fast enough to process data as it arrives. Techniques like model pruning, quantization, and distributed training can help improve scalability and efficiency, allowing the model to adapt to larger datasets and more complex tasks while maintaining performance.

Model Interpretability

Model interpretability is the extent to which the model's prediction can be understood by humans. In some industries, such as healthcare or finance, explaining how a decision was made is just as important as the decision itself.

Models like decision trees and linear regression are inherently interpretable. Meanwhile, complex models like deep neural networks require techniques such as SHAP (Shapley Additive Explanations) or LIME (Local Interpretable Model-agnostic Explanations) to enhance interpretability. Ensuring a balance between accuracy and interpretability can be crucial, especially in domains where decisions must be transparent and justifiable.

Tools and Frameworks for Implementing AI Models

An AI model implementation requires various tools and frameworks to cover the different stages of the machine learning lifecycle. This is what you can use when building, training, and maintaining AI models:

Popular AI Frameworks: TensorFlow, PyTorch, and Scikit-Learn are used in developing AI models. TensorFlow and PyTorch are both open-source and offer extensive support for deep learning. Scikit-Learn is ideal for beginners and lightweight applications. It provides simpler tools for classification, regression, clustering, and other traditional ML tasks.
AutoML Tools: Google AutoML, H2O.ai, and DataRobot automate the process of training and tuning machine learning models. They allow users with minimal coding experience to develop AI solutions. AutoML is particularly helpful in rapidly prototyping AI models and achieving good baseline performance with less effort.
MLOps and Deployment Tools: Platforms such as MLflow, Kubeflow, and AWS SageMaker support the deployment, monitoring, and lifecycle management of AI models. They help automate processes like model versioning, deployment pipelines, and monitoring for drift in model performance. By integrating MLOps practices, organizations can ensure that their models remain reliable and scalable in the long run.

How We Will Choose the Right AI Model for Your Project

Efficient choosing, training, and implementing AI models require specific expertise and profound experience. Having a reliable tech partner like Integrio makes the process much easier. We can build AI-powered web and mobile applications. Our specialists are proficient in making robust platforms and can help with the integration of intelligent automation to optimize business processes.

We choose AI models to correspond to business needs, ensuring technologies deliver substantial benefits. This is how we do it:

Understanding Your Business Objectives

We work closely with stakeholders to specify the desired outcomes. Let us know what you aim to improve within your application, whether it's customer service, operational efficiency, or decision-making. We will then establish the criteria for evaluating the model's performance accordingly.

Assessing Data Readiness

We analyze data quality, quantity, and structure to determine if it’s suitable for training AI models. If gaps or inconsistencies are found, we assist clients in improving data quality through data cleaning, augmentation, or collection processes. Understanding the data landscape ensures that our AI solutions can achieve reliable results.

Matching Model to Use Case

We select models based on the nature of the problem—be it classification, regression, or natural language processing. This stage may involve comparing different algorithms or using a combination to meet the requirements.

Experimentation and Prototyping

We build and test models quickly, iterating based on early results to fine-tune the approach. This allows for flexible adjustments and helps identify the most promising and secure solutions before full-scale deployment.

Evaluation and Iteration

Evaluation is an ongoing process. We continuously measure model performance using metrics relevant to the business goals, making refinements as needed.

Deployment and Support Strategy

Finally, our specialists in Integrio Systems ensure a smooth deployment and support strategy. When merging the AI solution into existing systems, we offer continuous support to keep the model updated and effective over time.