What is AI modeling?

Built-from-scratch AI models versus ready-made AI models

As you can see, there are many different AI models in use today. Everything from classic machine learning to deep learning, such as deep neural networks, convolutional neural networks and recurrent neural networks.

Taking these models and moving them into production is a tedious process. According to a Gartner survey, on average, only 48% of AI projects make it to production, and it takes eight months to go from AI prototype to production.

A notable difference between built-from-scratch AI models and ready-made AI models is the success rate of getting models into production. Ready-made models are an alternative to the modeling techniques we have discussed. They can speed up innovation with or without in-house data science teams – solving the AI talent gap.

Real-world applications of AI modeling

Businesses can't ignore AI modeling. Its tangible benefits go beyond speed and efficiency. AI handles repetitive tasks like data entry, freeing human teams to focus on more complex, strategic initiatives.

AI modeling’s unparalleled data analysis capabilities deliver insights crucial for competitive strategic planning. By automating processes and minimizing human errors, AI can significantly reduce operational costs. Finally, capabilities such as more precise targeting, optimized pricing strategies and enhanced customer experiences translate directly into increased profitability.

The real power of AI modeling shines in its application to solve real-world challenges:

• Business predictions. Companies use AI modeling to forecast sales trends, manage inventory efficiently and optimize supply chains. For instance, an AI model could predict seasonal product demand fluctuations by analyzing historical sales data and current market conditions.
• Health care innovations. AI modeling significantly impacts health care by supporting early diagnosis and providing personalized treatment strategies. For example, deep learning models can analyze medical imagery to detect anomalies faster and more accurately than traditional methods.
• Customer experience enhancements. Through NLP-powered chatbots, businesses provide instant, accurate customer service around the clock. AI models analyze customer interactions, identify preferences and then personalize communications, thereby increasing customer loyalty and satisfaction.
• Fraud and cyber-threat detection. Financial institutions use machine learning models, such as random forest algorithms, to detect and prevent fraudulent activities by recognizing patterns typical of fraud and anomalies humans might miss.

Trustworthy AI

It can be difficult to take something so complex as an AI model and convert it into information that is easily digestible and explainable for everyone involved. The ability to share what goes in and what comes out of a model is beneficial to regulatory compliance. Just another consideration for AI modeling and AI ethics.

Model cards serve as comprehensive documentation tools that provide essential insights into various aspects of models. They highlight key indicators such as accuracy, fairness and model drift – the latter referring to the decline in model performance as external conditions evolve.

Additionally, model cards encompass governance details, including the date of the last modification, contributors and responsible parties. This information is crucial for organizations to effectively manage and rectify any abnormal model performance internally.

The model usage section of these cards outlines the intended applications, identifies out-of-scope use cases and specifies limitations. These elements are particularly important as the fields of transparency and model auditing are anticipated to become regulated aspects of business operations.