Monitoring and Optimizing Performance of Custom AI Solutions: Best Practices

Artificial Intelligence (AI) and Machine Learning (ML) technologies are revolutionizing the way companies operate, allowing them to optimize operational efficiency, improve decision-making, and enhance the customer experience. To ensure that these solutions are performing optimally, it is essential to establish best practices for monitoring and optimizing performance. This blog post outlines four key best practices for establishing an optimized AI system in your organization.

1.Establish a Quality Assurance Process

The first step in optimizing AI performance is to set up a quality assurance process.

This involves creating a system to monitor the accuracy of the AI model and guarantee that it is in agreement with the fundamental truth. For instance, if you develop a loan approval model to predict which customers are likely to repay a loan, you should create a system to monitor the accuracy of the model and make sure that it is correctly approving loans to customers who will return the money.

2.Leverage Generative AI

Generative AI can be used to transform marketing and the customer experience by allowing companies to generate personalized and tailor-made content on a large scale. Natural language processing (NLP) and machine learning models, such as GPT-3 and Codex, can be used to generate code and automate software development processes.

By leveraging generative AI, companies can improve customer satisfaction, loyalty, and ROI.

3.Partner with an AI Quality Management Provider

Partnering with an AI quality management provider can help data scientists and DevOps engineers answer critical questions about operational and AI quality in real time. New Relic has partnered with TruEra, a recognized leader in AI quality management and the performance management of machine learning models.

4.Monitor Performance Regularly

Finally, it is important to monitor performance regularly.

This involves tracking metrics such as accuracy, latency, throughput, memory usage, and other key performance indicators (KPIs). By monitoring performance regularly, companies can identify areas for improvement and ensure that their AI solutions are performing optimally.