Choosing the Right AI Model or Building your Own

Now that you have decided on your use-case and have prepared and cleaned up your data, you can now move to building and scaling your AI models. Before we begin, let's first understand what AI models are. Imagine a processor where you can insert or add some material, and after it's done processing, it transforms the input into something else. Yet, when you try to look inside to see how it's done, it is extremely complex and quite ambiguous. AI models work in a similar way. They function more or less like functions that take in input and give you an output. The amazing thing about these models is that they can be taught to give you the kind of output you need without really understanding or focusing on how they do it.

If you decide to build your AI model, there are a few things you would have to understand. There are many different types of models. Some models do prediction, others do classification, and each is suitable for different use cases. You may build a model from scratch, especially if you have a unique use-case, although generally, you don't have to. You would most likely be fine-tuning an existing model if not using an existing one outright. Fine-tuning simply means teaching a model that knows how to do something general to be more specific.

Let's get more specific and understand the different types of models you can choose from:

Supervised Learning Models:

These models are trained on labeled data, where the input and desired output are provided. They are commonly used for tasks like image recognition, natural language processing, and predictive analytics. Popular supervised learning models include:

• Regression models (for predicting continuous values): A retail company could use a regression model to predict future sales based on various factors like marketing campaigns, seasonality, and economic indicators.
• Classification models (for categorizing data into classes): A healthcare provider could use a classification model to predict the risk of developing certain diseases based on patient data like medical history, genetic factors, and lifestyle habits.
• Neural networks (for complex pattern recognition): An e-commerce company could use neural networks for image recognition to automatically categorize and tag product images for better search and recommendation capabilities.

Unsupervised Learning Models:

These models are trained on unlabeled data, where the algorithm must find patterns and relationships without predefined outputs. They are often used for clustering, dimensionality reduction, and anomaly detection. Examples include:

• Clustering algorithms (e.g., K-Means, DBSCAN): A marketing firm could use clustering to segment customers based on their purchasing behavior and preferences, allowing for more targeted campaigns.
• Autoencoders: A manufacturing company could use autoencoders to detect anomalies or defects in product images, enabling better quality control.
• Association rule mining: A grocery store chain could use association rule mining to analyze customer purchase data and identify frequently bought item combinations, helping with product placement and promotions.

Reinforcement Learning Models: These models learn through trial and error, receiving feedback in the form of rewards or punishments for their actions. They are particularly useful for decision-making problems, robotics, and game-playing. Popular reinforcement learning models include:

• Q-Learning: A logistics company could use Q-Learning to optimize delivery routes and schedules, minimizing costs and improving efficiency.
• Deep Q-Networks (DQN): A gaming company could use DQN to develop intelligent agents that can learn and adapt to different game scenarios, enhancing the player experience.
• Policy Gradient methods: A robotics company could use policy gradient methods to train robots to perform complex tasks like assembly or navigation in dynamic environments.

Conclusion:

Choosing the right model depends on your specific use case, data characteristics, and project requirements. If you have a unique problem or cannot find a suitable pre-trained model, building a custom model from scratch may be necessary. However, for many applications, fine-tuning or using pre-trained models can be a more efficient and cost-effective approach.

In the next section, we'll explore the process of building and fine-tuning AI models, as well as best practices for deploying and maintaining them in production environments.

Call to Action:

Has your organization implemented any of these AI model types? What challenges did you face, and what lessons did you learn? Share your experiences in the comments below!

P.S. This article is part of a series on the Roadmap to Implementing AI within Your Business. Stay tuned for more insights and practical tips!