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Chapter 6A: Introduction to Model Monitoring and Observability

Introduction

In the context of MLOps, observability refers to the ability to understand and gain deep insights into the internal state of machine learning and operational systems by analysing their external outputs, such as metrics, logs, and traces. Unlike traditional monitoring—which focuses on checking if systems are running and alerting when they are not—observability enables teams to diagnose why systems behave a certain way and uncover hidden performance bottlenecks or failure patterns.

Over time, observability has evolved from reactive monitoring to a proactive, data-driven discipline. It encompasses a holistic approach, integrating telemetry collection, real-time analysis, visualisations, and predictive diagnostics to deliver a comprehensive view of system health and behaviour.

Monitoring vs. Observability

  • Monitoring is about tracking known problems through predefined metrics and alerts. Monitoring answers the question: "Is it working?"
  • Observability is about uncovering unknowns by correlating disparate signals to reveal system behavior and root causes. Observability answers the question: "Why isn’t it working?"

Why Observability Matters for Government Agencies

For government agencies managing critical infrastructure, citizen-facing services, or secure environments, observability is not just a luxury—it's a necessity. With increasing demands for transparency, uptime, security, and resilience, observability enables:

  • Early detection of system failures or performance issues
  • Quicker root cause identification and remediation
  • Optimisation of resources and budget
  • Increased confidence in deploying AI/ML-powered services at scale

AWS Observability Maturity Model

The AWS Observability Maturity Model serves as an essential framework for agencies looking to optimise their workload observability and management processes. Through this model, agencies can follow a structured roadmap to:

  • Assess their current observability capabilities
  • Identify maturity gaps
  • Progress toward more sophisticated observability practices

As systems become more complex (e.g., containerised microservices, hybrid architectures, AI/ML workloads), observability must evolve in tandem. Therefore, this model serves as a useful resource for government agencies and organisations to build toward a more resilient and insight-driven IT infrastructure.

Stages of the AWS Observability Maturity Model

Stage 1: Foundational Monitoring – Collecting Telemetry Data

At this stage, organisations establish a baseline by collecting core telemetry: metrics, logs, and traces. Monitoring is often siloed, with different teams using disparate tools, formats, and methods, leading to inefficiencies and blind spots.

Key focus areas:

  • Instrument workloads for basic telemetry collection
  • Identify critical workloads and essential metrics
  • Begin unifying monitoring tools across teams

Stage 2: Intermediate Monitoring – Telemetry Analysis and Insights

In this stage, telemetry data is not just collected but analysed for actionable insights. Agencies develop dashboards, alerting mechanisms, and triage workflows. While teams can now investigate issues more effectively, MTTR (Mean Time to Resolution) can still vary due to alert fatigue and manual processes.

Key enhancements:

  • Implement alert prioritisation based on severity
  • Reduce noise through smarter alerting policies
  • Begin correlating telemetry data for improved troubleshooting

Stage 3: Advanced Observability – Correlation and Anomaly Detection

In this stage, agencies start correlating metrics, logs, and traces for automated root cause analysis. AI/ML techniques are introduced to detect anomalies and auto-remediate recurring incidents. The result is reduced downtime, better service levels, and higher system reliability.

Capabilities at this stage:

  • Cross-signal correlation for 360° situational awareness
  • ML-powered anomaly detection
  • Tight integration with incident management tools

Stage 4: Proactive Observability – Automatic and Proactive Root Cause Identification

At this highest maturity level, observability becomes predictive and self-improving. Well-trained AI models use historical and real-time data to predict issues before they occur, propose resolutions, and trigger automation.

Key innovations:

  • Generative AI dynamically generates relevant dashboards
  • AIOps systems proactively detect and resolve incidents
  • Observability data drives continuous process and architecture optimisation

Building an Observability Strategy

To benefit from observability, government agencies must take a step-by-step and strategic approach to achieve the highest maturity level, aligned with organisational goals.

Step 1: Identify Your Current Maturity Stage

Agencies should begin by evaluating their current observability capabilities based on the stages in the AWS Observability Maturity Model. This includes analysing the agency's:

  • Data collection methods (logs, metrics, traces)
  • Monitoring and alerting systems
  • Cross-team collaboration
  • Automation readiness

Step 2: Define Observability Goals Aligned with Agency Objectives

The agency's goals should reflect both technical and mission-critical outcomes. Some examples include reducing incident response time, improving system uptime, ensuring compliance, or enabling faster AI/ML deployment cycles.

Step 3: Identify Key Metrics (KPIs)

ML practitioners should identify and define the performance indicators that matter most to your agency. These could include different metrics, including but not limited to:

  • System latency
  • Error rates
  • Request throughput
  • Resource utilisation

A detailed breakdown of different key metrics for model-level and system-level observability will be covered in the following subchapter.

Step 4: Choose the Right Tools and Technologies

Select the most suitable observability tools based on your specific use case, environment (e.g., on-prem, AWS, hybrid), and existing workflows. A detailed review of different observability tools and solutions will be covered in the following subchapter.

Step 5: Foster a Culture that Values Observability

It is not enough to just deploy the right technologies as agencies must learn to develop a culture of observability by:

  • Training teams on observability best practices
  • Encouraging cross-functional collaboration
  • Promoting continuous improvement through data-driven insights

This cultural shift is especially crucial in ensuring that observability is established as a long-term, core operational capability—rather than an afterthought.

Concluding Remarks

The AWS Observability Maturity Model serves as a comprehensive guide for government agencies and organisations to advance their operational insight capabilities. By understanding where they are and where they need to go, agencies can:

  • Reduce operational risk
  • Enhance citizen services
  • Improve IT efficiency
  • Deliver more reliable AI/ML systems

For more information about the AWS Observability Maturity Model, practitioners are highly encouraged to visit the official guide (https://aws-observability.github.io/observability-best-practices/guides/observability-maturity-model/) written by the team at AWS.