Different Success / Evaluation Metrics for AI / ML Products

Success metrics for AI and ML products

In this post, you will learn about some of the common success metrics which can be used for measuring the success of AI / ML (machine learning) / DS (data science) initiatives / products. If you are one of the AI / ML stakeholders, you would want to get hold of these metrics in order to apply right metrics in right business use cases. Business leaders do want to know and maximise the return on investments (ROI) from AI / ML investments. 

Here is the list of success metrics for AI / DS / ML initiatives:

  • Business value metrics / Key performance indicators (KPIs): Business value metrics such as operating cash flow (OCF) or the monthly recurring revenue (MRR) can be used to measure AI / ML product / solution contribution to overall business success. Gartner recommends that KPIs be defined before the AI project is deployed (as a baseline) and again after project completion to determine the resulting benefit compared to the baseline. KPI is nothing but a metric which can be used to measure performance against the baseline performance. AI solutions are mostly offered as part of product offerings. Thus, one can use product operational KPIs to measure the effectiveness of AI / ML. Some of the product operational KPIs include cost per acquisition, number of new sessions, users’ retention rate, users’ conversion rate, number of sign-ups etc. Here is the hypothesis: The more pronounced the AI capabilities in the product / solutions are, the greater their influence on operational KPIs. In case the product is largely an AI-based product, then the business value metrics of product can be directly associated with effectiveness of AI/ML solutions embedded in the product.
  • AI / ML Technical metrics: Many a time, product operational KPIs or business value metrics is not enough to measure the effectiveness of AI solutions. For example, enhanced usage of email can’t be enough to measure the effectiveness of spam filter until proper isolation is done in some manner. Thus, it is recommended to use AI / ML technical metrics in conjunction with business KPIs to measure the effectiveness of AI / ML solutions. Here are different kinds of technical metrics which could be used with different kind of AI/ML solutions:
    • Classification accuracy
    • Root mean square error (RMSE)
    • Mean absolute error (MAE)
    • Sensitivity / specificity average
  • Number of business questions / problems / requests addressed; Responsiveness to the business questions / problems / requests
  • Number of departments / business users supported / adopted the solution
  • Number of models deployed in production
  • Volume of data analyzed
  • Number of white papers published

References

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data Science and Machine Learning / Deep Learning. In addition, I am also passionate about various different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia etc and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc. I would love to connect with you on Linkedin.
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