AI has fundamentally changed the way business functions. Adoption of AI has more than doubled in the past five years, with enterprises engaging in increasingly advanced practices to scale and accelerate AI applications to production. As ML models become increasingly complex and integral to critical decision-making processes, ensuring their optimal performance and reliability has become a paramount concern for technology leaders. This is where model observability and ML monitoring step in, playing a pivotal role in empowering organizations to gain comprehensive visibility into their ML models' behavior and performance in real-world applications. In this article, we delve into the fundamental distinctions between model observability and ML monitoring, shedding light on their unique attributes and functionalities. Moreover, we explore the best practices that enable data scientists and ML engineers to harness the full potential of these practices, ensuring seamless model deployment, rapid issue detection, and continual enhancement.
What is Model Monitoring?
Model monitoring is a fundamental practice in machine learning that focuses on the systematic observation and evaluation of ML models during their deployment and operation in live applications. As ML models are employed in critical decision-making processes across various domains, ensuring their continued effectiveness and reliability is extremely important.
Model monitoring involves the continuous collection and analysis of key performance metrics related to the ML model's behavior, accuracy, and overall performance. These metrics may include prediction accuracy, data and concept drift, feature importance, and model response time, among others. By monitoring these metrics in real-time, data scientists and ML engineers gain invaluable insights into the model's ongoing performance, promptly identifying any issues that may arise. An automatic model monitoring system can trigger event-driven retraining so that models stay accurate continually and manual interventions are minimized.
By adopting robust model monitoring practices, organizations can proactively address performance issues, optimize models for changing data patterns, and ensure that their ML models remain accurate and reliable throughout their operational life cycle.
What is Observability in ML? Gaining Insights into the Black Box
As ML models grow increasingly sophisticated and are applied to diverse domains, understanding how they make decisions and interpret data becomes essential. Observability refers to the ability to understand how well complex ML systems are working, based on their external outputs.
Unlike traditional software applications, ML models often operate as "black boxes," making it challenging to comprehend the decision-making processes that drive their predictions. Observability is an attempt to address this challenge by offering data scientists and stakeholders a window into the model's behavior, reasoning, and evolving patterns.
The goal of an ML observability practice is to:
- Know the system’s baseline behavior
- Get alerts when things break or when anomalies happen
- Understand why those unexpected events happened
Observability in machine learning involves various techniques and tools that enable researchers to analyze and interpret model outputs, identify potential biases, and understand how the model responds to different inputs. This level of visibility empowers organizations to assess model performance, ensure ethical AI practices, and troubleshoot issues effectively.
To achieve observability, practitioners often use techniques such as model explainability (SHAP is a common method for this), which elucidates in human terms how a model reaches its conclusions. Interpretability, on the other hand, is the ability to have high model transparency and understanding of exactly why and how models generate responses. This requires interpreting the model’s weights and features to analyze outputs. This can come at the cost of performance, thus sometimes organizations will opt for a simpler model that can be easily interpreted than a more complex model.
Observability is a relatively new topic in ML, driven by business needs for transparency, trustworthiness and responsible AI. Current methods for explainability are imperfect, and there’s lots of research still to be done to improve our understanding of ML systems, especially in the realm of generative AI.
ML Monitoring vs ML Observability
In the dynamic world of machine learning, where models are continuously deployed and updated, ensuring their optimal performance and reliability is of utmost importance. This is where machine learning monitoring and machine learning observability come into play, as two distinct but complementary practices that offer different perspectives on model management.
Machine learning monitoring primarily focuses on tracking and analyzing the performance metrics of deployed ML models in real-world scenarios. It involves collecting and evaluating key indicators, such as prediction accuracy, to detect potential issues like model drift or data inconsistencies. The main goal of monitoring is to maintain models' effectiveness—or ‘health’--and promptly address deviations from expected behavior.
On the other hand, machine learning observability delves deeper into the inner workings of ML models, providing comprehensive visibility into their decision-making processes and the reasons behind their predictions. Observability techniques, such as model interpretability and sensitivity analysis, shed light on how models process inputs and identify patterns or biases in their outputs. This level of insight empowers data scientists and stakeholders to understand model behavior, diagnose root causes of issues, and improve overall model performance.
In summary, machine learning monitoring focuses on tracking performance metrics for operational excellence, while machine learning observability aims to provide interpretability and visibility into the black box of ML models for a deeper understanding of their behavior. By incorporating both practices, organizations can ensure the robustness, transparency, and reliability of their machine learning deployments.
As organizations grapple with the practical realities of embedding AI in their processes, there is a growing recognition that observability must be streamlined, continuous and automated. User-facing live applications must be continuously updated according to event triggers, and this requires factory-like efficiency processes. Data scientists need visibility to know what is happening with the model in production, so they can create fast iterations.
Making ML systems observable requires various technical capabilities to be implemented, including:
- The storage of data inputs and model outputs
- the capture of training data statistics and feedback of the actual results, often at a lag of hours or even days from the time the model was executed
- Comparisons of different statistics of data and model performance
- Identification of drift on both the feature level and model level
- Model behavior analysis to explain unknown scenarios
- Trigger threshold creation and updates for notifications and/or actions, allowing quick model updates and/or related execution rules
Clearly, there is a lengthy and highly technical process required to implement effective observability.
That is why it is critical for modern enterprises to implement automated observability into their systems, not as a 3rd-party add-on component or a complex DIY project, but as part of a fully integrated and streamlined solution. Automation allows the data scientist to focus their core effort on understanding the data and developing models, rather than having to deal with all the technical aspects of managing the observability and operational aspects of the production system.
Observability for ML equips enterprises with a comprehensive set of tools and practices to monitor, detect, and address issues that could impact ML model performance from a business standpoint. Observability is a critical component for complex, always-on user-facing business applications to protect AI assets and mitigate risks effectively.
Automated machine learning observability streamlines operations and enables data science teams to focus on business goals, ultimately setting the stage for an ML factory approach to AI-powered business.