Both AI and machine learning are skyrocketing as companies undergo digital transformation. As models and data pipelines become more complex, they become increasingly difficult to manage. Another challenge is that because MLOps and AIOps are relatively new disciplines, people often confuse them. We’re here to solve that problem. What is MLOps and AIOps? In this part of our MLOps series, we’ll define AIOps and explore the differences between MLOps and AIOps.

Table of contents:

What Is Machine Learning Operations (MLOps)?
What Is Artificial Intelligence Operations (AIOps)?
Why Is AIOps Important?
Who Needs AIOps?
What Are Use Cases for AIOps?
What’s the Difference Between AIOps and MLOps?
Want More Information About AIOps?


Machine learning operations is the process of creating, deploying, and maintaining machine learning models. The discipline combines machine learning, DevOps, and data engineering to uncover faster, simpler, and more effective ways to productize machine learning.

A typical MLOps process has several different steps:

  1. Defining your business goal
  2. Collecting relevant data
  3. Cleaning and processing data
  4. Building the model (Or using a ModelOps platform with ready-to-deploy models)
  5. Deploying the modelThis requires unifying standards and processes across multiple teams, first by deciding on business objectives and continuing through data acquisition, model development, and deployment. Additionally, AutoML frameworks can make machine learning processes more accessible to non-experts.The most important thing to keep in mind here, as described in MLOps: Continuous delivery and automation pipelines in machine learning, is that data science and ML are becoming core capabilities for solving complex real-world problems, transforming industries. You can navigate complex business problems by doing the work to put these processes in place.


The AIOps definition, according to Gartner who first coined the term, is as follows:

AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.

With a focus on increasing IT operations efficiency, AIOps systems intelligently identify the root causes of IT incidents and provide high-quality diagnostic information that enables tech teams to work towards a resolution.


There are a lot of different technologies that make up your IT infrastructure. To complicate things further, your IT infrastructure is shared across a wide variety of business services and applications. If it’s become a challenge to keep up with all the changes to these services and applications—it may be time to turn to AIOps.

AIOps has clear business benefits, including:

Improved collaboration: AIOps platforms facilitate collaboration by bringing clarity to workflows with reports and dashboards that outline necessary tasks and requirements. AIOps also streamlines communication by grouping and prioritizing IT alerts.

Increased ROI: AIOps decreases an organization’s mean time to recovery (MTTR). This limits costly downtime and increases overall productivity and efficiency.

Successful digital transformation: To stay ahead, especially in today’s digital landscape, organizations must always be innovating. AIOps fosters innovation by lifting some weight off your IT team. With AIOps your greatest tech minds will spend less time resolving IT tickets and monitoring usage patterns and more time focusing on large-scale digital transformation and innovation.


Many businesses can benefit from implementing AIOps, which in many ways, acts as ITOps with an AI layer. If you’re working with multi-tiered environments, AIOps makes it easier to manage requests and monitor systems that run the business. It simplifies the processes that come along with managing and supporting thousands of applications and users. It improves your visibility into IT systems and automates operations processes. This way, you can better manage performance, uncover problems, and solve issues faster.


AIOps can support a wide range of IT operations processes. Here’s a closer look at different use cases for AIOps.

Performance monitoring

AIOps enables organizations to build a more proactive approach to performance monitoring. Reactive monitoring can potentially cost businesses hundreds of thousands of dollars in lost revenue. With AIOps, rather than reacting to issues after they arise, organizations can identify, remediate and optimize performance issues in real-time—before they become system-wide problems.

Infrastructure topology

Most organizations use static infrastructure maps, which offer limited insights and can quickly become outdated. AIOps solutions, on the other hand, enable dynamic topology. Dynamic topology captures the resources and their relationships as the environment changes. In addition to providing near-real-time visibility, dynamic topology grants the ability to compare the current topology with historical versions. Organizations that utilize AIOps-led infrastructure typology can answer both “What happened?” and “What is happening?” with details on how topology and status have changed over time.

Noise reduction

Alert fatigue, when an overwhelming number of alerts causes an individual to become desensitized to them, is a huge problem in incident response. AIOps minimizes alert fatigue by preventing alert storms from overwhelming your employees. AIOps solutions filter and correlate meaningful data to suppress low-priority alerts and group together alerts that are related. By delivering intelligent alerts that are prioritized based on user and business impact, AIOps solutions limit the noise and ensure your critical alerts get noticed.

Anomaly detection

Detecting and fixing problems as your IT infrastructure becomes more dynamic is no easy feat. Trying to understand the root cause of a potential issue can be extremely difficult to do, which makes anomaly detection critical in many cases. AIOps makes anomaly detection faster and ultimately, more effective. That’s because AIOps can monitor the difference between the value of a KPI and what the machine learning model predicts. Then, it can flag deviations that wreak havoc.


In the hunt for greater operational efficiency, organizations around the world are increasingly turning to automation solutions. This means, more and more, tech leaders are digging into both MLOps and AIOps.

While both machine learning and artificial intelligence play a big role in helping companies achieve operational efficiency, MLOps and AIOps are very different disciplines involving different technologies and processes. Most importantly, they serve different goals.

You can’t afford to confuse the two. So, when in doubt ask yourself this question, “What do I want to automate? Processes or machines?”

AIOps increases the efficiency in IT operations by using machine learning to automate incident management and machine diagnostics.

MLOps is the practice of bringing machine learning models into production. It makes it easier to bridge the gap between data ops and infrastructure teams to get models into production faster. Unlike AIOps, MLOps doesn’t directly refer to a machine learning capability.

So, in other words, AIOps automates machines while MLOps standardizes processes.

However, despite the distinct differences, there are overlaps in the teams and skills required to successfully implement AIOps and MLOps. Before diving into one or the other, it’s worth looking into where they overlap to see what resources can do double duty by serving both disciplines. For example, an overarching ModelOps platform with ready-to-deploy models can accelerate both the MLOps and AIOps processes.


If you need more information about MLOps and MLOps platforms dive into our blog series to get an in-depth look at MLOps including the MLOps best practices you should follow. Plus, keep your eye on the Veritone blog as we continue to publish content on MLOps, AIOps, and related topics.