Solve machine learning model monitoring challenges with model performance management (MPM)

​Translator | Li Rui

Reviewer | Sun Shujuan

In the past few years, the digitalization of the world has brought unique opportunities and challenges to organizations and enterprises. While the boom in data has provided more opportunities to improve decision-making accuracy, analyzing and leveraging this information is now more time-consuming and expensive. As a result, businesses of all sizes are deploying machine learning (ML) models that can process large amounts of data and identify patterns and correlations that are often overlooked by analysts or take unreasonable time. These models have the power to enhance decision-making and drive superior business results. For example, some machine learning models can make highly accurate predictions about how quickly a specific product will sell over the next year to improve marketing and inventory planning. Other businesses are able to identify fraudulent transactions that can result in millions of dollars in lost revenue.

But with the increasing reliance on machine learning models, the need to monitor model performance and build trust in artificial intelligence has become more urgent. Without machine learning model monitoring, MLOps and data science teams will face the following problems:

• Inconsistent model performance. This can fluctuate because machine learning models are trained on historical data, which may differ from the real data they see in production.
• Lack of control and debuggability. Because complex machine learning systems are opaque, practitioners may not know enough about the machine learning model to know how to fix it if something goes wrong.
• Examples of bias. Machine learning models can amplify hidden biases in the data they are trained on, exposing businesses to legal and reputational risks, and potentially leading to harmful outcomes for consumers.
• Improve the performance of machine learning. Because it’s difficult to understand and track what improvements are needed, machine learning models don’t receive further investment after the initial release.

MLOps teams are also more likely to lack confidence in their models, which can lead to more time spent on the project and more errors. Machine learning model monitoring enables developers to debug models in pilot and production processes to catch issues as they occur. This is the most efficient way to achieve explainable, fair, and ethical AI solutions, which are crucial in today’s world. Let’s say a bank is using a machine learning system to approve loans. They may receive customer complaints asking the bank why a certain loan was denied, and the bank would be responsible for explaining why the model made that decision. Tracking the answer to this question is nearly impossible without a proper monitoring solution.

Whether a machine learning model is responsible for predicting fraud, approving loans, or targeting ads, small changes that occur can cause model drift, inaccurate reporting, or bias—all of which can result in lost revenue and impact brand credibility.

Challenges in Model Monitoring Today

Unfortunately, machine learning model monitoring has become more complex due to the variety and number of machine learning models that organizations rely on today. Machine learning models now serve a wide range of use cases, such as anti-money laundering, job matching, clinical diagnostics, and planetary surveillance. They also come in many different representations (tables, time series, text, images, video and audio). While these models can handle the vast amounts of data businesses need to work with, tracking them is much more difficult and expensive.

Some enterprises have deployed traditional infrastructure monitoring solutions designed to support broad operational visibility to overcome these challenges. Others try to create their own tools in-house. In either case, these solutions often fail to meet the unique needs of machine learning systems. Unlike traditional software systems, the performance of machine learning systems is uncertain and depends on various factors such as seasonality, new user behavior trends, and often extremely high-dimensional upstream data systems. For example, a perfectly functional ad model may need to be updated when a new holiday season arrives. Similarly, a machine learning model trained to recommend content in the United States may not translate well to international user registrations. Alternatively, businesses often face issues with being unable to scale due to outdated models, wasted production troubleshooting time, and additional costs for in-house tool maintenance.

To enable visibility and explainability in machine learning models and overcome common model monitoring challenges, enterprises need solutions that can easily monitor, interpret, analyze, and improve machine learning models and adopt models Performance Management (MPM).

How Model Performance Management (MPM) Addresses Performance and Bias

Model Performance Management (MPM) is a centralized control system at the center of the machine learning workflow that tracks performance at all stages of the model lifecycle. performance, and closing the machine learning feedback loop. With Model Performance Management (MPM), enterprises can discover deep, actionable insights through explanation and root cause analysis, while immediately surfacing machine learning performance issues to avoid negative business impact.

Model Performance Management (MPM) continuously and automatically re-evaluates model business value and performance, issues alerts on model performance in production, and helps developers proactively respond at the first sign of bias. Because Model Performance Management (MPM) tracks the behavior of a model from training to release, it can also explain what factors lead to a certain prediction. Combining model monitoring with other pillars of machine learning observability such as explainability and model fairness provides machine learning engineers and data scientists with a comprehensive toolkit that can be embedded into their machine learning workflows, and provides a single dashboard across model validation and monitoring use cases. Enterprises benefit from model performance management (MPM) not only because it makes model monitoring more efficient, but also because it reduces instances of bias that lead to costly regulatory fines or reputational damage. Machine learning models require continuous model monitoring and retraining throughout their lifecycle. Model Performance Management (MPM) allows developers to not only gain confidence and greater efficiency in their models, but also understand and validate the reasons and processes behind their AI results.

Original title: ​​Solving for ML Model Monitoring Challenges with Model Performance Management (MPM) ​​ , Author: Krishnaram Kenthapadi​

The above is the detailed content of Solve machine learning model monitoring challenges with model performance management (MPM). For more information, please follow other related articles on the PHP Chinese website!