AIOps: Building, Scaling and Governing AI Models in Production
May 14, 2025
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5
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Blog
At its core, AIOps is about governance across AI workloads—ensuring data, models, and associated applications are developed, deployed, monitored, and maintained in a governed and measurable manner. This oversight extends throughout the entire lifecycle of an AI project, from experimentation to production—and beyond.
To better understand AIOps, it helps to look at the three types of AI workloads it supports:
Machine Learning Operations (MLOps), Foundation Model Operations (FMOps), and Generative AI Operations (GenAIOps).
Machine Learning forms the base, and as organisations advance in their AI journey, they often extend their operational needs to include foundation models and generative models—each bringing its own set of challenges around deployment, monitoring, and governance.
AIOps provides the common ground to manage all of these effectively across the AI lifecycle.
Machine Learning (ML) has been around for over 75 years, but in recent decades it has become omnipresent—from personalised shopping recommendations to fraud detection.
The ML lifecycle includes stages like building, training, testing, deploying, monitoring and eventually retraining models. While model building may feel like the crux of innovation, keeping that model performing in production over time is far more complex—and far more critical.
This is exactly where AIOps steps in. It provides the automation and processes needed to manage machine learning models as they move from experimentation into live environments—and ensures they stay relevant and accurate as real-world conditions evolve.
Deploying ML models at scale isn’t the same as deploying traditional software. It introduces a whole new set of challenges—technical, organisational, and procedural.
Many organisations struggle because ML adoption often demands rethinking team structures, processes, and skillsets. The need for cross-functional collaboration between data scientists, data engineers, backend developers, compliance officers, and cloud specialists becomes non-negotiable. Furthermore, every model is unique—its data requirements, training logic, drift patterns, and performance metrics will differ.
Without the right foundations, businesses risk investing in models that fail to scale, degrade quickly, or even breach ethical and regulatory standards.
AIOps is not just a toolchain—it’s an approach that blends infrastructure, human capability, and process clarity. Think of it as a triangle:
This approach becomes especially vital in production environments, where the margin for error narrows significantly. For example, monitoring is not just about uptime; it involves tracking CPU utilisation, HTTP request volume, data drift, bias detection, and overall prediction quality.
Every ML project begins not with code—but with a business problem.
Not every business decision needs a model. If you're determining driving licence eligibility based solely on age, a few lines of rule-based code will do. But when the decision involves dozens of variables—medical history, driving records, behavioural data—ML starts to make sense.
The process typically unfolds in three phases.
The first step is identifying the right use case and reframing it as a machine learning problem. It must lead to a measurable outcome—say, a 20% increase in straight-through processing or a reduction in manual interventions.
Once the problem is defined, data collection begins. This is often the most time-intensive stage of the project. Contrary to popular belief, model success correlates more strongly with data quality than data quantity. The training data must represent real-world conditions the model is expected to encounter. Missing, inconsistent, or skewed data leads to poor generalisability.
Next comes feature engineering, where raw data is cleaned, standardised, and reshaped into a format suitable for model consumption. Imagine using an "expiry date" as a training feature. Dates might appear in multiple formats (DDMMYYYY, MM/DD/YYYY) and inconsistencies across data entries can confuse the model. A better approach may be to convert expiry dates into a simple numeric value—like “days until expiry.”
After preprocessing, model training begins. The team experiments with different algorithms, neural networks, or cloud services to build a candidate model. But building is only half the task—evaluation is what validates whether the model is fit for purpose.
Evaluation metrics depend heavily on the domain. A social media recommender might perform acceptably at 60% accuracy. In finance or healthcare, the minimum bar is typically much higher—often above 90%.
If the model underperforms, the team returns to the data —augmenting it, refining it, and retraining to align better with the target problem.
When a model meets both technical and business objectives, it can move to production. However, real-world environments are volatile. Models degrade over time due to changing inputs, behaviours, or external conditions.
That’s why ongoing evaluation, monitoring, and retraining is critical. AIOps ensures the system adapts to change, extending the model’s useful life while maintaining performance and compliance.
DevOps primarily focuses on code and system integration. AIOps includes that—but also brings data and models into the equation.
In DevOps, a pipeline might be triggered when a new version of code is pushed. In AIOps, the pipeline needs to react to:
This gives rise to a training pipeline, which automates stages such as:
With ML, Foundation Models, and Generative AI entering production at scale, traditional DevOps pipelines are no longer sufficient. AIOps extends the paradigm by targeting three key objectives:
AI needs continuous integration and delivery pipelines, just like code. But the assets are more complex:
Operationalising AI securely requires attention to:
AI systems can no longer be black boxes. Regulatory bodies demand transparency, and businesses need to explain not just what a model predicted, but why.
A strong governance layer must include:
As AI systems grow more dynamic and context-aware, we're entering the era of agentic AI—systems that don't just respond to inputs but can proactively reason, plan, and act across multiple steps and changing goals. These agents introduce a new operational layer: managing actions, memory, and evolving objectives in real time.
Alongside this, prompt engineering has emerged as a critical interface layer, especially with foundation and generative models. Prompts are no longer one-off queries—they’re structured, versioned inputs that can significantly impact model behaviour. Managing prompt templates, tracking prompt drift, and evaluating their performance under different contexts is fast becoming a part of production workflows.
FMOps and GenAIOps expands to accommodate these needs—incorporating tools for prompt lifecycle management, agent observability, and safety guardrails for autonomous decisions. It's a shift from managing models to managing intelligent, adaptive systems.
The shift to AI-powered systems is inevitable—but without operational discipline, it’s also risky. AIOps brings the same rigour that transformed software engineering into the world of artificial intelligence.
By integrating automation, security, and governance into every stage of the AI lifecycle, AIOps ensures that models don’t just get built—they thrive in production, adapt to change, and deliver lasting business value.
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