In the age of Artificial Intelligence (AI) and Machine Learning (ML), the race to integration is a real thing. As businesses continue to find ways they can leverage these technologies, the importance of quality performance cannot be overstated. For this reason, KPIs are essential…

The two most important assets of AI and ML are:

1. How they benefit the service delivery/experience to the end-user 

2. How they enhance back-end functionality 

These two components are what put these technologies on a pedestal to a point where, should they fail in the future when dependency peaks, it will be catastrophic.

Where KPIs Come In

So we know that KPIs were created for the very reason to measure and evaluate the performance and impact of digital systems, but we should also note that KPIs alone are not sufficient for ensuring your system's success. Again, we’re preaching the “drawing board” approach where businesses outline and focus on their unique goals because it’s not a one-size-fits-all situation.

Continuous improvement is the name of the game, and while KPIs cannot directly guarantee the success of your systems, they play a crucial role in monitoring and guiding your progress.

Machine Learning - User-Friendly Industry Disruptor

Of course, these systems are user-friendly, but when it comes to leveraging these tools from a business standpoint, certain things cannot be ignored when competing in an AI-hungry industry:

1. Machine Learning Takes on Your Objectives: 

The integration of Machine Learning initiatives is meant to simplify your current processes and enhance results. When you consult with a team of AI architects, they’re not going to show you an entirely new way to do your job; rather, they’re pinpointing the more efficient methodology for your current process.

For instance, let’s look at sales: a salesman typically has to make around 50 contacts before they get a single person who’s interested. Let’s say he does 50 cold emails per day and 50 cold calls, landing about 10 interested prospects per work week. 

If the company then implements AI and ML algorithms that can analyze data and recognize the behaviours and characteristics of successful clients, the system can generate lead scores or rankings that indicate the likelihood of a lead converting into a sale. The sales team can then automate lead scoring and prioritize their efforts based on the highest-ranked leads, instead of manually sifting through and wasting touchpoints with unqualified prospects.

2. Predictive Analytics and Pattern Recognition Are Your Bread & Butter

The healthcare sector is a great example of a well-rounded execution through AI and ML strategizing. Hospitals are extremely data-intensive in nature, and that data is highly valuable when it comes to things like patient care, operational efficiency, and medical research as a whole. 

Something really interesting that predictive analytics and pattern recognition have done to revolutionize this landscape already is early diagnosis. Basically, by identifying patterns in a patient's data, Machine Learning can outline potential risks with that patient at an early stage.

Now the healthcare sector is certainly something that deserves a much more thorough analysis, but this gives a sense of the standards that this aspect of your system needs to be performing at.

3. Agile Implementation

This has been a staple in software development for decades as a means to implement solutions as quickly and efficiently as possible. For a company just beginning its journey with AI and ML, this is a great method to familiarize yourself with these technologies and to determine how cohesive they are with your current processes.

Implementing Machine Learning systems and Artificial Intelligence is highly transformative in an organization and is a lot more comprehensive of a stage than planning. Agile is a great approach for this reason as it allows adaptation to happen gradually, and the system can be refined consistently as the needs of its users evolve.

So, we’ve highlighted some key factors that businesses need to keep in mind when preparing for AI and ML integration, but now we have to identify what specifically can ensure your systems are going to consistently perform up to standard.

10 KPIs for Machine Learning:

Evaluation Metrics

1. Accuracy: How accurate are the predictions made by the Machine Learning model compared to actual outcomes?

   • Accuracy is a crucial measure of the system's performance and its ability to make correct predictions. It provides an overall assessment of the model's effectiveness.

2. Recall: How well does the recall of the Machine Learning model evaluate the proportion of true positive predictions compared to all actual positive instances in the dataset?

   • Recall measures the ML model's ability to capture all relevant positive instances, avoiding false negatives which leads us to the next point.

3. Precision: How does the precision of the Machine Learning model assess the proportion of true positive predictions?

   • Precision is significant in cases where the cost of false positives is high. It evaluates the system's ability to make accurate positive predictions, minimizing false positives.

Performance Metrics:

1. F1 Score: Balances precision and recall, providing a single metric that represents the model's overall performance.

2. Mean Absolute Error (MAE): Measures the average difference between predicted and actual values, which will indicate the model's average prediction error rate.

3. Mean Squared Error (MSE): Computes the average squared difference between predicted and actual values, which will emphasize larger errors than MAE would. 

4. Root Mean Squared Error (RMSE): Takes the square root of MSE, then provides a metric on the original scale of the targeted variable.

5. R-squared (R2) Score: Indicates the proportion of the variance in the target variable that can be explained by the model's predictions.

Evaluation Techniques:

1. Precision-Recall Curve: This plots the trade-off between precision and recall at different prediction thresholds, and helps to set an optimal threshold for the ML model's performance.

2. Receiver Operating Characteristic (ROC) Curve: Illustrates the trade-off between the true positive rate and the false positive rate at various classification thresholds, which ultimately aids in the selection of an appropriate threshold.

Moving Forward

The next step with your system is to experiment and evaluate. Planning is crucial but it can’t provide you with results. Focus on your unique goals. Performance evaluations might involve running simulations or real market tests and then analyzing the data you collect to dictate which metrics best suit your system.

Whatever it looks like for you, consider the nature of your system, the industry you're operating in, and the outcomes you want to achieve.

Written By Ben Brown

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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.