AIOps vs. MLOps: How are they different?

What is AIOps?

AIOps is the use of AI to help perform IT operations tasks.

For example, a team that uses AIOps might use AI to analyze the alerts generated by its monitoring tools and then prioritize the alerts so that the team knows which ones to focus on. Or an AIOps tool could automatically find and fix an application that has crashed, using AI to determine the cause of the problem and the proper remediation.

By most modern definitions, AIOps stands for AI for IT operations. However, the term originated as a shorthand for algorithmic IT operations, a phrase coined by Gartner in 2016.

What is MLOps?

Short for machine learning operations, MLOps refers to practices for designing, developing, training, testing and deploying machine learning (ML) models.

The core idea behind MLOps is that it systematizes the process of building and operating ML models. In this way, MLOps aims to bring consistency, repeatability and scalability to complex practices. It also facilitates collaboration between various stakeholders in the ML development and deployment process.

Conceptually, MLOps is similar to the software development lifecycle, which defines a consistent set of processes for creating and deploying software applications.

MLOps is also comparable to DevOps, which encourages collaboration between software developers (who design, write and test code) and IT operations teams (who deploy and manage software in production). In a similar vein, MLOps encourages data engineers, software developers and IT engineers to collaborate to create high-quality models.

Also like DevOps, MLOps isn't a specific set of rigidly defined practices. It's more of a high-level framework that encourages organizations to approach ML model development in a consistent way, while leaving it up to teams to decide exactly which tools and processes they'll implement.

Benefits and use cases

AIOps and MLOps can both benefit organizations that make use of AI, but they do so in different ways.

AIOps benefits

The main benefits of AIOps include these advantages:

• Complex automations. AIOps can automate IT processes -- particularly those that are too complex or involve too many variables to be automated using simple scripts or if-this-then-that logic. AIOps can handle more complex automations by using AI to make decisions.
• Automated remediation. This enables IT systems to fix themselves under certain circumstances.
• Intelligent alert management. AIOps helps reduce the volume of alerts that IT engineers must manually assess and respond to, since AI-powered tools can resolve many alerts themselves.
• Less toil. Automations translate to less time spent by IT teams on tedious tasks.

MLOps benefits

Meanwhile, MLOps offers the following key benefits:

• Consistency. MLOps offers a consistent, predictable, repeatable approach to creating and managing ML models.
• Faster, more reliable change management. Teams can update or enhance models faster and with fewer disruptions, thanks to the consistency behind model development processes.
• Greater collaboration. MLOps helps distinct stakeholders -- data engineers, software developers, IT engineers and even compliance officers and security teams -- to work in concert in creating efficient, secure models.
• Faster time to market. Using the efficient processes that MLOps offers, organizations can develop and deploy ML models faster.

Challenges of AIOps vs. MLOps

AIOps and MLOps also present distinct challenges.

AIOps challenges

For AIOps, these challenges include the following:

• Data challenges. Limited data availability or quality can cause AI-powered tools to perform inadequately.
• Error risks. AI tools can make incorrect or undesirable decisions, just as humans can. However, mistakes by AI tools can be particularly hazardous when workflows are fully automated without an opportunity for humans to identify and mitigate errors.
• Integration complexity. Implementing AIOps often requires integrating disparate IT processes or systems, such as ticketing and cybersecurity response tools, which don't always connect easily using AI-powered workflows.

MLOps challenges

These are the chief challenges of MLOps:

• MLOps process failures. MLOps processes can fail or take longer than expected due to unpredictable problems. For example, model training might fail if models can't access appropriate training data, but it's often impossible to know ahead of time exactly how a model will respond to a particular training data set.
• Security risks. Security challenges can arise at all stages of the MLOps lifecycle -- whether it's protecting sensitive information within training data or mitigating prompt injection risks once models are running.
• Model retraining. Even with the consistency that MLOps introduces, retraining models can be a time-consuming endeavor due to the complexity of the training process -- which is typically necessary to improve the scope of information that models can work with. 

AIOps vs. MLOps best practices

Despite focusing on different aspects of AI adoption or implementation, AIOps and MLOps share certain best practices in common.

The most important thing is maintaining high standards of data quality. AIOps tools and MLOps processes will both perform poorly when they have access to limited data or when the data they ingest is of low quality.

Providing adequate human oversight is also a best practice in both contexts. Although many workflows within AIOps and MLOps can be automated, it's important to identify those that are high-stakes or high-risk and should require human oversight. For example, an AIOps workflow might require manual approval before permanently deleting a database, and a new ML model could be reviewed by engineers before being deployed into production, even if it passes automated tests.

Defining clear objectives is also important for both AIOps and MLOps. Organizations should treat both practices as a means to an end, but they must understand exactly what that end is. It would be unwise to pursue either AIOps or MLOps without knowing which benefits an organization aims to achieve.

Chris Tozzi is a freelance writer, research adviser, and professor of IT and society. He has previously worked as a journalist and Linux systems administrator.