The benefits of an artificial intelligence and machine learning model assessment

Artificial intelligence and machine learning (AI/ML) models are storming the business landscape in nearly every sector; providing fast, powerful insights that can only be found in big data. Questions that were previously never thought possible to answer, are being answered. However, as powerful as these models are, they take a considerable amount of work to create with attention needed for every step. A small issue in any one of the steps can lead to biased or incorrect results. An external validation of a model can identify issues, find corrections or improvements and provide confidence in the data and insights gathered. The AI/ML model assessment delivers a variety of benefits, such as improved model performance, reducing biases and providing confidence in the model.

What is an AI/ML model assessment?

A model assessment is a thorough review of a model that utilizes AI/ML though discussions, code and documentation review and analysis of a model’s output. The model assessment process follows the entire life cycle of a model.

Each step of a model’s life cycle has decisions that can affect the outcome of the model. For the assessment we review each step; where we collect information about what was done, ask probing questions, conduct further research and provide written feedback for potential improvements.

Improving model performance

The performance of an AI/ML model can be measured in several ways: overall accuracy of the model, number of false-positives, compute time of the model, along with many other metrics. Regardless of the metric, a model’s performance can be hindered by sub-optimal choices at any step of the model’s creation. A full assessment can identify opportunities to optimize model performance in the current model and future iterations. Often data slowly changes over time, which can cause models to underperform compared to when they were first trained. An assessment includes discussion on techniques to monitor the ongoing performance and establish criteria for retraining or recrafting of a model.

The data science world is constantly evolving, new algorithms or techniques are always appearing. A model’s current algorithm might have been the best performing for the task as of a year ago, but a new algorithm might have come out of research in the meantime. Along with the current state model design, potential new techniques and algorithms will be discussed for future improvement of a model.