In our previous discussion about monitoring, we covered everything valuable except for the most important “elephant in the room.” While monitoring is keeping your model sane, you have something to protect your project from “why” questions: why did your model tell me to eat more vegetables? How can I believe the model remembers my cousin’s birthday date correctly? How did my pet’s name information help to train this model? And so on.
Artificial intelligence observability is key to transparency and keeping your end users informed about anything they want to know about how the model works and what’s happening inside.
This feature fights with “black boxes” logic, ethical considerations, biases, and other challenges of ML models. In this article, we’ll go beyond the theory a bit and explain a framework using exemplary tools and an ML observability platform you might use.
Observability refers to the ability to fully understand the internal state of a system by analyzing its external outputs. In the context of AI/ML and data systems, observability provides the tools, techniques, and infrastructure needed to monitor, troubleshoot, and optimize the health, performance, and reliability of these systems in real-time.
Unlike traditional monitoring, which focuses on predefined metrics and alerts, observability dives deeper by enabling root cause analysis, anomaly detection, and predictive insights. It allows teams to proactively identify and resolve issues, ensuring that systems remain reliable, scalable, and aligned with business objectives.
Observability in AI is about understanding why something is broken and how to fix it. Think of it like a car dashboard but for your AI models, commonly with automated reactions and event prediction features. Here are the main parts that make it work:
Data is the fuel for AI, and if it’s dirty or changes over time, the model can go off track. Tools like Monte Carlo or Great Expectations check for missing values, weird patterns, or if the data starts drifting away from what the model was trained on.
Once the model is live, we need to make sure it’s doing its job. Is it still accurate? Is it treating everyone fairly? If the world changes—like during a pandemic—and the model starts making bad predictions, that’s called model drift. Tools like Arize AI or WhyLabs help us catch these issues.
AI systems are like assembly lines—data comes in, gets processed, and then the model makes predictions. If one step breaks, the whole thing can fail. There are platforms like Dagster or Flyte specialized in analyzing the entire pipeline of the AI ecosystem.
Opensource is here as well: 71% of companies now use tools like Prometheus and OpenTelemetry to monitor their data pipelines. And that brings value even for beginners.
If something goes wrong—like predictions taking too long or error rates spiking—you get an alert. Datadog or Prometheus are great for this. And while AI/ML isn’t a magic bullet for observability yet, it’s getting better at things like root cause analysis and anomaly detection.
Big AI systems can act like a black box sometimes. Why did it deny someone a loan? Why did it recommend that product? Tools like SHAP or Fiddler AI help us explain the model’s decisions. This is super important for building trust, especially since 33% of organizations now consider observability business-critical at the C-suite level.
Model can accidentally learn biases from the data, and that’s a big no-no. The big platforms like Fairlearn or IBM AI Fairness 360 make sure the model treats everyone fairly. It’s like having an ethics coach for your project. And with regulations tightening, this is becoming a must-have for companies.
AI systems can be attacked or leak sensitive data, so we need to keep them secure. Tools like Robust Intelligence or Protect AI help us detect adversarial attacks and ensure compliance with privacy laws like GDPR. Think of it as a security guard for your AI system.
For this case, Dysnix provides a set of DevSecOps services to address the security and privacy challenges of the projects. Find out more about that from the case below:
All this data needs to be presented in a way that’s easy to understand. Dashboards and reports help us see the big picture and share insights with the team. Grafana or Streamlit are perfect for this. Did you know that 76% of organizations use open-source solutions like Grafana for visualization?
Finally, for each project, there must be an instrument helping to learn from mistakes and improve. If users, stats, or coordinators say the model’s predictions are off, we collect that feedback, retrain the model, and make it better. Tools like LangSmith or Labelbox help us close the loop.
This is especially important for large language models (LLMs), where feedback is key to keeping them relevant.
To determine if it’s the right time for your project to start the observability journey, explore these payoff benefits of your implementation efforts.
And to motivate you even more, let’s talk about the dark side of NOT implementing observability in your project.
AI observability is no longer just a technical framework, a whim of rich corporations that can afford expensive improvements. Now, it’s the linchpin for scaling AI systems, ensuring trust, and driving business value.
As Baris Gultekin, Head of AI at Snowflake, highlights, 2025 is the year AI observability goes mainstream, becoming the "missing puzzle piece" for explainability and production readiness.
Observability is evolving into a strategic enabler, helping projects prevent, explain, and solve hallucinations, bias, and inefficiencies while unlocking innovation through proactive monitoring and guardrails.
Unified platforms, AI-driven insights, and open standards like OpenTelemetry are reshaping the landscape, making observability a competitive advantage. Ignoring it risks not only operational failures but also reputational damage in an increasingly AI-driven world.
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