Council Post: ML observability vs ML monitoring: The tactical/strategic paradox

ML observability is being dubbed the ‘holy grail’, a rage going on since 2021. ML systems are at the front and centre, being used for mission-critical functions more than ever – creating a pressing need for model monitoring and observability. 

The fact that Machine learning models are dynamic in nature, makes it all the more challenging to ‘tame’ them. They are immensely complex, are exposed to dynamic real-world data and operate at scale in terms of input complexity and volume. Their performance needs to be monitored, or it degrades over time.

Practitioners would want to be the first to know when a problem arises and work on resolving it quickly. Tools with dashboards, alert systems, performance benchmarks and logs were set to maintain the required accuracy and model performance. This practice is referred to as ML monitoring.

ML monitoring in machine learning is the method of tracking the performance metrics of a model from development to production. Monitoring encompasses establishing alerts on key model performance metrics such as accuracy and drift.

But this reactive approach is not sustainable for complex, volatile systems responsible for core functions and driving daily business decisions. There is a need for real-time capabilities that make it possible to explore granular visibility into the model and navigate to cause from the effects. That is the true challenge model observability handles. 

ML observability: Root-cause analysis across the ML project lifecycle

ML observability provides deep insights into the model state and health. It entails tracking/observing the performance and functioning of ML systems across their lifecycle, right from when it’s being built, to pre and post-production, but ML observability also brings a proactive approach to investigating model issues and highlighting the root cause of the problem.

Observability covers a more extensive scope than ML monitoring – it understands why the problem exists, and the best way to resolve it. 

ML observability examines the outcomes of the system as a whole rather than just the monitors for each system component.

Why should ML observability be a part of your deployment strategy?

Observability handles an ML model’s health diagnostics by investigating the correlation between inputs, system predictions and the environment to provide an understanding during an outage.

Effective model performance management requires more than detecting emerging issues. It requires capabilities that allow a deeper, proactive approach to root cause analysis of problems before they significantly impact the business or its customers. Clear, granular visibility into the root cause of problems provides additional risk controls to users, for implementing changes in the model accordingly. 

Moreover, with rising stringent regulations, enterprises need to provide answers on how the ML platform arrived at a decision. They must be ready to undergo system audits, documentation/ data protocols, transparency and monitoring for regulatory analysis. 

ML observability provides a quick, easily interpretable visualisation, with the ability to slice and dice into the problem, suitable for multiple stakeholders, even non-technical ones. It helps to pinpoint why the model is not performing as expected in production and gives clarity on rectifying it – be it retraining the model, updating datasets, adding new features, etc.

Does ML observability eliminate the need for monitoring?

Although the definitions and terms overlap, observability does not eliminate the need for monitoring. When both co-exist, an ecosystem where ‘when’ and ‘why’ an issue occurred becomes essential for a volatile system where changes are complex and constant. Hence, this radically changes the ML journey – 

When evaluating a platform, both observability and monitoring should be essential components of your checklist. There are plenty of platforms available in the market today – while some offer observability and monitoring as a part of the offering, there are platforms which also offer additional essential components like explainability, audit, etc. 

 Applying the above principles creates a feedback loop between the ML workflow and all its users. This creates a common ground for all stakeholders involved – Data science/ ML, Business, Regulatory and product teams. 

This enables teams to confidently deliver trustworthy models and continually scale and improve models to gain a strategic ML advantage.

A good ML Observability tool can provide a common framework for all stakeholders to understand, debug, monitor and deliver the much-needed framework for AI Governance. 

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.