Nora Winkens on October 12, 2021
With the advancements of technology, the use of both AI (Artificial Intelligence) and ML (Machine Learning) has risen with leaps and bounds as companies undergo digital transformation. With this churn in the technology ecosystem models and pipelines are becoming more complex and it’s getting harder to manage them. There’s another challenge that exists, is that MLOps and AIOps are comparatively new disciplines and can be confusing at times. Well, we are here to solve that problem. In this blog, we will discuss and explore the differences between MLOps and AIOps.
What Is Machine Learning Operations (Mlops)?
MIops is the process of creating, deploying, and maintaining machine learning models. This discipline is the combination of ML, DevOps, and data engineering to find faster, simpler and more efficient ways to productize machine learning. Nowadays many AI developing companies are venturing into this domain due to the features MIops provide.
A standard MLOps process has several diverse steps:
- Establishing your business goal
- Cleaning and processing data
- Developing the model or using the Model Ops platform
- Deploying the model – This requires the combination of standards and processes across various teams. The first step is to decide the business objectives and continue through data acquisition, model development, and deployment.
What Are Artificial Intelligence Operations (Aiops)?
Gartner was the first to coin the definition of AIOps and according to them, the definition is as follows.
AIOps is the combination of big data and machine learning so that it can automate IT operations processes which include event correlation, anomaly detection, and casualty determination. With the emphasis on increasing the efficiency of the IT operation, AIOps systems intelligently identify the cause of IT incidents and in return provide high-quality diagnostics information that helps each tech team to work towards a resolution.
Why Is Aiops Important?
There are an array of technologies that make up your IT infrastructure. The complicated thing about this is that the IT infrastructure is shared across a wide range of businesses services and applications. If in the future it becomes a challenge to up with the changes to these applications and services, it could be the time to turn to AIOps.
AIOps has clear business benefits, including:
Improved collaboration: AIOps platforms help in collaboration by providing clarity to the workflows by giving reports and dashboards that outline requirements and necessary tasks. AIOps also streamline communication by prioritizing and grouping IT alerts.
Increased ROI: The use of AIOps reduces an organization’s mean time to recovery (MTTR). This eliminates costly downtime and increases productivity and efficiency.
Successful digital transformation: To sustain in today’s digital environment organizations must always be innovating. AIOps allows organizations to innovate by doing all the heavy lifting in their IT team. With it, your IT staff gets to spend the least time resolving IT tickets and monitoring usage patterns. The employees can then spend more time focusing on large-scale digital transformation and innovation.
What’s The Difference Between Mlops And AIops?
To attain a higher level of operational efficiency businesses all over the world are turning towards automation solutions. This has caught the attention of tech leaders and they are digging into both MLOps and AIOps.
Both technologies AI and ML play a very important role in helping companies machine operation efficiency, however, MLOps and AIOps are very distinct disciplines involving multiple technologies and processes. What’s most important is that they serve different goals.
People can easily get confused between the two. So, whenever in doubt, ask yourself this question. “What exactly do I want to automate? Processes or machines?”
AIOps – it increases the efficiency of IT operations by using ML to automate incident management and machine diagnostics.
MLOps – it is the use of ML models in production. This helps in bridging the gap between data ops and infrastructure teams to quickly get models into production. Unlike AIOps, MLOps does not directly relate to a machine learning capability.
So, in simpler words, AIOps automates machines while MLOps standardizes processes.
Even with its differences, there are overlaps in skills and teams required to completely and successfully implement AIOps and MLOps. Before implementing any one of these disciplines it is worth looking into where they overlap, to see what resources can work both ways by serving both disciplines. For example, the use of the ModelOps platform with ready-to-use models can accelerate both MLOps and AIOps processes.
Collected at: https://datafloq.com/read/whats-difference-between-aiops-mlops/18523