Machine learning (ML) and AI have the potential to drive incredible innovation and efficiency, but stakeholders also have concerns about the impact if ML models don’t work as intended. These fears range from violating AI regulations to damaging their organization’s reputation and negatively affecting human lives. According to a 2021 survey, respondents worry about the following consequences of AI bias:
fear they may lose customer trust.
are concerned their brand reputation may suffer, resulting in media and social media backlash.
worry about increased regulatory scrutiny.
have concerns regarding a loss of employee trust.
are concerned that ML bias will conflict with personal ethics.
fear the possibility of lawsuits.
worry about impacts on profits and shareholder value.
To avoid unintended consequences and catch issues early on, ML teams should establish a model monitoring framework from the start. In this article, we’ll cover what is monitoring in machine learning, why it matters, and how a model monitoring framework can set your ML solutions up for success.
ML model monitoring, a subset of AI model monitoring, refers to the ongoing efforts to improve the accuracy and effectiveness of machine learning models. It involves continuously tracking and analyzing the performance and behavior of machine learning models in production environments to ensure they remain accurate, reliable, and effective over time. This includes identifying issues such as data drift, model degradation, and anomalies, providing action insights for developers to maintain optimal model performance.
Proactive model monitoring techniques are essential for reducing downtime, ensuring consistent model performance, and improving overall effectiveness. Machine learning models are prone to challenges such as data drift, propagating biases, and performance degradation. As models constantly ingest new and dynamic data, monitoring methods must evolve.
Model monitoring isn’t a “set-it-and-forget-it” endeavor but must no longer feel overwhelming. Leveraging the right machine learning monitoring tools allows teams to navigate the complexity of opaque models, making it easier to understand the “how” and “why” behind their predictions. These tools enable ML teams to identify the root causes of issues and implement timely fixes, ensuring optimal performance and reliability
One of the significant challenges businesses face is that data teams often work in silos, limiting effective communication and collaboration. A well-designed ML model monitoring framework addresses this issue by breaking down silos and fostering teamwork when problems first arise — before they escalate into more complex challenges.
As a core component of your machine learning operations (MLOps) team, a machine learning model monitoring framework establishes a feedback loop that includes data scientists, ML engineers, and business operations. This approach ensures that all stakeholders are aligned and informed. Leveraging an AI Observability platform, team members can access model alerts and actionable insights, enabling them to analyze root causes and resolve issues efficiently.
Left to their own devices, machine learning models can make incorrect inferences from patterns and develop biases that may harm your reputation and end users. In other words, models are not immune to developing human prejudices. How does a ML model work, and what makes them susceptible to developing model bias? There are several reasons why models can become biased, including:
For businesses that rely on machine learning models for day-to-day operations as well as innovation, inaccuracies can have disastrous consequences. For example, in the following survey, 36% of businesses reported being negatively impacted by machine learning bias. Out of the businesses that were affected:
lost revenue
lost customers
lost employees
incurred legal fees from lawsuits
lost customer trust
Even enterprise-scale organizations working in the most prominent industries are at risk of suffering from machine learning biases. For example, the U.S. healthcare system, Facebook, and Amazon had to correct their ML models to account for AI fairness:
In 2019, a study found that a healthcare risk-prediction algorithm — which was used to evaluate over 200 million people in the US — demonstrated racial bias. The root of the issue was that the proxy data set contained patterns that reflected disparate care between white and black Americans.
In particular, the algorithm focused on how much patients spent on healthcare in the past to determine their current risk for chronic conditions. Using this spending data, the algorithm determined that since white patients spent more on healthcare, they were more likely to be at risk for chronic illnesses. In reality, black patients spent less on healthcare due to a variety of factors that were unrelated to their actual symptoms — like their income levels and confidence in the healthcare system.
This bias made it more challenging for black patients to receive care for chronic conditions, even though they had a high level of need. This not only harmed patients, but also weakened confidence in the fairness of the healthcare system.
In 2019, Facebook neglected to enforce constitutional requirements that prevent advertisers from directly targeting audiences based on gender, race, religion, and other protected classes. During this period, Facebook’s algorithm learned that advertisers were targeting these protected classes for different products and services, such as real estate. As a result, Facebook’s ad algorithm reflected the bias of advertisers and prioritized showing real estate ads to white audiences over members of minority groups. This limited housing opportunities for groups who have historically had limited chances for owning property.
This learned bias violated the Fair Housing Act, and as a result, the U.S. Department of Housing filed a lawsuit against the social media company.
In 2015, Amazon realized that its new automated job candidate review system had a noticeable gender bias. The issue began in the model’s training. After analyzing the application patterns throughout a 10-year period in Amazon’s history, the model recognized a pattern that most past team members were men.
Acting on this pattern, Amazon’s machine learning model identified and rejected women’s resumes. Graduates of women’s colleges and members of gender-specific extracurricular activities, such as a women's soccer team, were affected by this bias. These errors rejected candidates that could have had valuable experience and portrayed Amazon and the tech industry as a whole in a negative light.
An AI Observability platform makes it possible for each member of your MLOps team to identify and resolve model issues efficiently and at scale. From a unified dashboard, team members can uncover and share insights, and perform root cause analysis to understand how and why models make the predictions they do. Fiddler’s AI Observability platform features best-in-class machine learning model monitoring tools, including:
Having a dedicated AI Observability platform reduces your “time-to” factors: your time to market, your time to value, and your time to resolution.
Model monitoring metrics provide critical insights into whether your machine learning model performs as expected. These metrics help identify performance issues, ensure accuracy, and maintain reliability. The five key metric categories are:
These metrics evaluate the performance of classification models by measuring how accurately they predict categorical outcomes. Common metrics include precision, recall, F1-score, and accuracy. For example, these metrics are essential in tasks like spam detection or medical diagnosis.
Regression metrics assess how well a model predicts continuous values. Key metrics include mean absolute error (MAE), mean squared error (MSE), and R-squared. These are particularly important in applications like sales forecasting or pricing predictions.
Statistical metrics help evaluate the underlying data distribution and model assumptions. They include measures like standard deviation, correlation coefficients, and p-values. These metrics are critical in ensuring your model aligns with your data's statistical properties.
NLP metrics assess the performance of models working with text or language data. Examples include BLEU (for translation accuracy), perplexity (for language models), and sentiment accuracy. These metrics are vital for chatbots, sentiment analysis, and language translation applications.
Deep learning metrics evaluate complex neural networks, focusing on model convergence, loss reduction, and layer-wise performance. Metrics such as cross-entropy loss, activation analysis, and epoch accuracy are commonly used. These metrics are essential for image recognition, speech processing, and autonomous systems.
These metric groups will have varying priorities depending on your specific project. For example, statistical accuracy is imperative when running a forecasting model.
Effectively monitoring machine learning models is essential for maintaining trust, transparency, and consistent performance. A robust model monitoring framework enables organizations to detect issues early, avoid unintended consequences, and ensure models align with both business goals and ethical standards.
To successfully monitor your ML model, you need a comprehensive framework that unites all relevant stakeholders. This framework should encompass your models, your teams, and an AI Observability platform that offers the tools and insights necessary to keep everyone informed and aligned.
Explore how the Fiddler AI Observability platform can help in implementing a strong monitoring framework and build trust and transparency in your ML models.
