Artificial Intelligence (AI) and machine learning (ML) have been buzzwords for the past decade, and rightly so. AI/ML models have the power to revolutionize industries by automating decision-making,streamlining processes, and improving productivity. However, as more companies rely on AI/ML models to run their businesses, the need for monitoring these models becomes increasingly important and complex. There’s an important evolution happening around tabular-data-based ML model monitoring as companies try to move from basic models and basic monitoring to high-scale sophisticated models that require a new generation of model monitoring at scale. Adding to this is the now rapidly emerging interest in large language models (LLMs) in the enterprise, and yet no real ability to monitor them given the nature of these models and the known reliability problems and risks that are still yet to be solved even in the consumer context. Effective monitoring now with the stakes so high, becomes more important than ever, which we will explore here.
ML Model Monitoring
Tabular data is the traditional form of data that has been used in ML for many years. This type of data is structured, with rows and columns, and is easily understood by humans. ML models are trained on this type of data and are often used in business applications, such as failure prediction, credit scoring, or fraud detection.
ML model monitoring involves tracking model performance metrics such as accuracy, precision, and recall for classification tasks, to ensure that the model performs as expected. If the model’s performance deviates from the expected performance metrics, it can be an indicator of input data drift, also known as feature drift, which means that the model is no longer receiving data that is representative of the training data. In this case, the model may need to be retrained on new data.
Many organizations had started monitoring these tabular-data-based models for performance, but many organizations still haven’t had the tools to do this at high scale, to support businesses that are running feature-rich, high-transaction models across multiple clouds and data sources. There are fraud detection models, and then there are high-stakes, high-scale fraud detection models (credit card fraud, insurance fraud, identity theft, and so on) that need to look at infinite performance dimensions in granular detail.
Large Language Model Monitoring
In recent years, and much more rapidly in recent months, large language models (LLMs) have started to emerge and become more prevalent – mostly outside the enterprise context (for now). LLMs are deep learning models that are trained on vast amounts of text data and can generate human-like responses to questions in natural language. These models have revolutionized the field of natural language processing (NLP) and have many practical applications, such as chatbots, language translation, and content generation.
LLM monitoring is more complex than traditional ML model monitoring because LLMs can be used to solve a wide variety of tasks, from answering questions and producing summaries to generating novel text or code. LLMs are trained for a multitude of tasks rather than just one, and often with a very large corpus of text data. As a result, it can be difficult to determine what constitutes “good” or “bad” performance across a wide variety of metrics and tasks. In LLM monitoring, metrics such as perplexity, which measures the model’s ability to predict the next word in a sequence, and diversity, which measures the variety of responses generated by the model, are used to assess performance. In addition, there are specific metrics that are used to assess the performance of the model on answering questions with or without provided context.
Language models embed text inputs into high-dimensional spaces. Each of these dimensions individually are difficult to interpret or provide meaning to. The embedding spaces of different language models are also different from one another and are impacted by the data the models were trained on, the complexity of text, and training parameters of the models themselves. Therefore, it is challenging to monitor the model’s own internal representation of text and how it connects various concepts together.
LLM monitoring also involves monitoring for bias and ethical considerations. LLMs are often trained on text data from the internet, which can contain biases and offensive language. If these biases and offensive language are not detected and addressed, the model’s responses could reflect these influences and offend or harm users.
Why LLM Monitoring is Crucial for Enterprises
LLM monitoring is crucial for enterprises that want to take advantage of ML models to run their businesses. In the case of chatbots, for example, a poorly performing LLM can lead to frustrated customers and lost business. In the case of content generation, a biased LLM can generate offensive or harmful content, leading to reputational damage and legal consequences.
There are also issues of confidentiality and security around LLMs in the enterprise. For example the security and privacy of data used in an LLM such as data points provided via prompts or uploads (documents, text, code, images etc), and other means, that may be confidential, proprietary, or otherwise restricted.
In addition, and perhaps most importantly, LLM monitoring is crucial for ensuring ethical considerations are met. LLMs have the power to influence people’s opinions and beliefs, and it is the responsibility of the enterprise to ensure that the model’s responses are fair, explainable and unbiased.
The evolution of tabular ML model monitoring to large language model monitoring reflects the increasing importance of monitoring ML models in today’s advanced AI world. As more enterprises rely on ML models to run their businesses, it is crucial to ensure that these models are performing as expected and that ethical considerations are met. LLM monitoring is complex, but it is essential for ensuring the success and reliability of ML models in the enterprise.
ML Monitoring for the High-Performance Enterprise
VIANOPS, our spring release of our ML model monitoring and observability platform, was developed against the backdrop of rapid AI advancements while staying grounded in our company mission at Vianai Systems to bring safe, reliable, human-centered AI systems to enterprises.
If you and your MLOps team would like to test out VIANOPS ML monitoring capabilities, sign up for our limited-time free trial.