AIOps vs. MLOps: Understanding the Key Differences

H1> AIOps vs MLOps: A Clear Overview of Their Distinct Value

Have you struggled to differentiate artificial intelligence for IT operations (AIOps) vs machine learning operations (MLOps)? Many IT leaders grapple with where these emerging technologies overlap and where they diverge.

This definitive guide will empower you to confidently architect AI-powered solutions tailored to your specific automation and innovation objectives. You’ll gain a clear understanding of the unique value propositions of AIOps and MLOps—cutting through the buzzwords to leverage both approaches.

First we’ll clarify precisely what capabilities AIOps and MLOps provide. We’ll then explore illustrative use cases for each across industries. Finally, we’ll dive into leading practices for successfully leveraging AIOps and MLOps in tandem to extract multiplier business value.

So let’s get started demystifying these game-changing yet often misunderstood technologies!

H2> What is AIOps

First coined by Gartner in 2016, the term AIOps refers to using artificial intelligence to automate and enhance IT operations. Leading industry analyst firm Gartner predicts that by 2025, 40% of enterprises will implement AIOps platforms to enable faster diagnoses and responses across increasingly complex hybrid infrastructure.

At its core, AIOps utilizes historical and real-time performance data plus advanced analytics to help IT teams work smarter and faster—letting algorithms handle repetitive work like filtering noise while people focus on high-value efforts like design thinking.

The key capabilities provided by AIOps solutions include:

Event Correlation

Identifying insights and patterns across massive volumes of disparate IT monitoring and event data

Causality Determination

Automatically surfacing root cause hypotheses from inside-out infrastructure topology analysis

Anomaly Detection

Proactively flagging outliers indicative of emerging risks or issues

Automated Remediation

Triggering corrective responses without human direction via integration with system ticketing, runbooks and more

By leveraging these AI-powered capabilities, AIOps delivers game-changing operational improvements including:

• 84% less alert noise through correlation and consolidation

• 57% faster incident response through automated root cause analysis

• 35% quicker security investigation powered by connecting threat narratives

• 50+ hours saved per quarter on compliance reporting through continuous controls validation

H2> What is MLOps

MLOps, or Machine Learning Operations, refers to the principles, practices and platforms that operationalize ML models to responsibly power data science applications and use cases.

Per leading technology research firm Algorithmia, over 50% of surveyed IT decision makers currently have machine learning models deployed in production, with MLOps adoption enabling them to realize greater scale, oversight and business returns from their AI investments.

The key principles that define MLOps include:

Collaboration – Ensuring alignment between data scientists, IT staff and business teams

Continuous Delivery – Institutionalizing velocity through the end-to-end ML lifecycle

Infrastructure Flexibility – Supporting consistency from labs to pipelines to serving

Auditability – Enabling explainability plus detecting bias and other issues

By embedding these principles, MLOps unlocks immense value including:

• 3x faster experimentation cycles to iterate models through build-measure-learn

• 69% faster deployment by operationalizing pipelines to scale environments

• 47% more model accuracy via ongoing data monitoring, drift detection and model management

Now let’s explore some representative use cases that showcase the real-world power of AIOps and MLOps…

H2> AIOps Use Cases

Leading video streaming company uses AIOps to optimize ultra-scale hybrid cloud infrastructure

With over 220 million worldwide subscribers, this popular streaming media service leverages AIOps to holistically monitor and improve performance across its massive, complex infrastructure that seamlessly spans co-located data centers, public cloud and edge locations.

By analyzing and baseline mapping over:

  • 17 billion online user sessions
  • 5.6 million servers and appliances
  • 1.3 zettabytes of media traffic

The AIOps solution empowers the streaming leader to automatically:

  • Detect and investigate emerging issues before they impact viewers
  • Identify trends and capacity constraints across geographies
  • Continuously validate security controls to maintain trusted platform status

Enabling smooth, resilient and compliant ultra-large-scale media delivery.

H2> MLOps Use Cases

Leading autonomous vehicle maker uses MLOps to accelerate self-driving R&D

This electric vehicle innovator leverages MLOps to develop bleeding-edge machine learning models that power its industry-leading self-driving software and sensors.

The MLOps platform provides:

  • Rapid experimentation for computer vision and reinforcement learning model training

  • Portable model containers to shift seamlessly from simulation to test track

  • Monitoring for sensor data drifts potentially affecting real-world performance

  • Ongoing model benchmarking and candidate promotion from R&D to product teams

Together these capabilities help the automotive trailblazer simultaneously achieve safety assurances and velocity as it races to define the future of transportation.

H2> Key Differences

While AIOps and MLOps share the overarching goal of optimizing processes and decisions via applied AI, these approaches differ significantly:


  • AIOps focuses on optimizing complex IT environments—think keeping systems healthy

  • MLOps focuses on managing the ML model development lifecycle—think breeding better algorithms


  • AIOps aims to use programmed intelligence for task automation—think auto-healing incidents

  • MLOps aims to operationalize learned intelligence for innovation—think launching new data-driven products and services

These distinct priorities also manifest in their tooling:

AIOps Toolchain

  • Event correlation engine

  • Noise reduction algorithms

  • Automated remediation playbooks

MLOps Toolchain

  • Experiment tracking systems

  • Model registry repositories

  • Canary deployments

H2> Architecting AIOps and MLOps For Complementary Capabilities

Savvy IT leaders don’t choose between AIOps and MLOps. Instead, they architect both platforms to empower complementary capabilities.

Let’s explore leading patterns for harnessing these sister technologies:

Streaming AIOps Insights Into the MLOps Data Lake

Since AIOps ingests and processes massive signal volumes from across IT environments, these rich observational datasets can improve context for model training and inference.

Triggering AIOps Automation Playbooks From MLOps Inferences

Conversely, by detecting insights like emerging security threats and infrastructure risks, MLOps models can initiate auto-remediation workflows via bidirectional integration with AIOps.

Applying AIOps for Compliance and Robustness of the MLOps Pipeline Infrastructure

By scanning configuration controls and monitoring pipelines, AIOps bakes-in reliability and compliance for the MLOps technical stack.

In these ways, AIOps oversight and automation helps mitigate the risk of MLOps innovation velocity—realizing best-of-both capabilities.

H2> The Future of AIOps and MLOps

As reflected by projected 25%+ CAGR industry growth rates, AIOps and MLOps represent foundational technologies for competitiveness across practically every sector.

Leading research firm Forrester declares that to optimize, secure and innovate, “enterprises need AIOps and MLOps platforms and practices.”

These approaches represent current best-practices for leveraging automation and machine learning. Over time, we anticipate AIOps and MLOps converging into unified AI suites—much as standalone DevOps tools have coalesced into consolidated platforms.

But for now, taking a use case-driven,outcome-focused approach to architecting joint AIOps and MLOps capabilities will maximize their complementary value in progressing through the AI maturity curve.

H2> In Closing

I hope this comprehensive yet understandable guide has empowered you to determine where investing in AIOps versus MLOps solutions makes the most sense for supercharging different processes across your organization.

The key is recognizing their distinct but powerfully complementary focus areas, data inputs and toolchains—leaning into those unique strengths while unifying governance and data-sharing across both platforms.

Only by modernizing, securing and optimizing the invisible architecture which runs business can you build atop powerful new innovations leveraging machine learning. Now go leverage AIOps and MLOps to transform IT into a real competitive advantage!