MLOps Vs AIOps - They Are The Same Right?
Tags: machinelearning, datascience, devops
Published: 2020-08-27 01:22:23



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In this tech-centric era, companies aim to extract maximum business value from their data in order to stay relevant and efficient via data-driven business decisions. In order to do so, they rely on Artificial Intelligence and Machine Learning. In fact, research shows that [artificial intelligence can increase business productivity by 40%](https://techjury.net/blog/ai-statistics/#gref)! Therefore, it is essential to implement the best tech practices and make the jobs of tech workers easier.

In the year 2020, many new ML and AI tools have been introduced to track data models and control data sets, but the work is still challenging. To optimize the complete ML production lifecycle, we need to bring in automation for the process between modeling and production. In this article, we will talk about that exact solution.

So let's start:

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DevOps --- MLOps --- AIOps
======================

MLOps, DataOps, AIOps --- there are so many "Ops" prefixed words it is getting difficult to manage.

As the name suggests, there are some connections to the concept of DevOps --- a group of concepts that aim to automate processes in any business. The main idea is to build, manage, maintain, test, and release softwares efficiently and accurately.

* * * * *

What Is MLOps?
==============

Machine Learning Operations, or MLOps, helps simplify the management, logistics, and deployment of machine learning models between operations teams and machine learning researchers.

It is basically DevOps applied to the field of machine learning. Obviously, some differences exist because of the way machine learning works. There is still an aspect of writing code and testing bugs. However, in the field of machine learning, you are training models with data and retraining with new data.

Not only does MLOps simplify integrations and make collaboration easier, but it also allows [data scientists and machine learning researchers ](https://towardsdatascience.com/4-must-have-skills-every-data-scientist-should-learn-8ab3f23bc325)to take on more projects and develop more models. With MLOps, the data training, testing, and deployment will often be automated, and therefore, the entire work process becomes much more efficient.

Right now, [15% of overall organizations are already advanced ML users](https://financesonline.com/machine-learning-statistics/) and about [65% of companies are planning to adopt machine learning](https://financesonline.com/machine-learning-statistics/) as it helps businesses in decision-making. Now here's why your organization needs MLOps. It follows the pattern similar to DevOps so there is a seamless integration between your operations process and the development cycle.

Before introducing MLOps into your organization, you have to consider a few things:

1.  Adoption: With any new paradigm, there will be a lot of work required to help employees adopt the new deployment model. There is a lot of trust required when you start automating testing and deployment. You will need to get both the data scientist and the operations IT teams on board. This usually happens through training, getting buy-in, and through a well-planned release of the new technology.
2.  Key Performance Indicators: Your KPIs must be clear and measurable to everyone on board --- from data science teams to the stakeholders --- so that they can understand how to use the insights and move forward.
3.  Monitoring: Since machine learning uses non-intuitive mathematical functions, there is a requirement of consistent monitoring to ensure that you are operating within the regulation and the programs are returning great quality of information.
4.  Compliance: The most important point is to ensure that GDPR doesn't strike your project. Therefore, your MLOps needs to be planned in such a manner that your programs become auditable.
5.  Built-In Bias: Automating machine learning model deployment and testing is not immune to human bias. Thus, it is important to make sure your team considers how it will work to develop a system that doesn't act unintentionally/intentionally[ bias toward specific groups of people](https://towardsdatascience.com/understanding-and-reducing-bias-in-machine-learning-6565e23900ac). Auditing your machine learning model regularly is one way to assess the output.

* * * * *

What Is AIOps?
==============

Artificial Intelligence for IT Operations, or AIOps, is another paradigm shift that allows machines to solve IT issues by themselves instead of requiring human assistance. It is a multi-layered technology that enhances the operations of IT. AIOps uses machine learning and analytics to analyze big data obtained via different tools. This allows the platform to automatically spot and react to IT issues in realtime. It works as continuous integration and deployment for the core technology functions in machine learning and big data.

Now the question is why your organization requires AIOps. Statistics suggest that [72% of IT organizations](https://docs.broadcom.com/doc/top-five-reasons-you-need-aiops) rely on different IT monitoring tools to support modern applications! Obviously businesses require these tools to manage day to day IT operations.

But there is more!

AIOps, in particular, not only helps in addressing complex IT challenges, but it also lets you cater to the[ exponential growth of data](https://www.theseattledataguy.com/5-great-libraries-manage-big-data-with-python/). Effective AIOps automate the entire operations process across hybrid environments and create an accurate inventory for machines to correlate data points independently. It is applied to machine learning to detect patterns and thus reduce noise.

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Where Is AIOps Helpful?
=======================

Noise reduction
---------------

If you have ever worked an on-call rotation on a development team, then you are probably all too aware of how many warnings and notifications you get. These are often well-intentioned as they are meant to help your team spot problems. However, sometimes the issues are transient and will fix themselves. Other tasks and operations might just need to be manually rerun. AIOps systems offers an automated AI system that can help reduce the number of issues that come to your operations teams. It does so by analyzing past issues and resolutions and attempts to reduce the tasks and notifications given to the operations teams.

If an issue can be resolved automatically or ignored the system will do so. Companies like [Resolve.io](https://resolve.io/aiops/noise-reduction) offer this type of service that can help your IT operations team better manage their systems and users.

Anomaly detection
-----------------

As discussed earlier, development teams will create notifications that detect specific signals. But this requires manual intervention. As in, someone often needs to create specific models to manage specific issues.

However, what about more unpredictable behavior? This is where machine learning and AIOps can step in again. Again, companies like Resolve.io have developed tools to help better detect anomalies and errors that your system may have never seen before.

System mapping and dependency management
----------------------------------------

Third-party cloud providers have simplified a lot of development, but this has also brought a whole new set of problems.

For example, serverless has made deploying infrastructure in some ways easier and cheaper. You can essentially create an API with very low-code. With this push for serverless comes a whole set of new issues like observability and system mapping.

With all these different technical pieces, on-premise, cloud, and third-party services, how do you manage dependencies and testing? It can be hard to see the impact of all your changes when you deploy your code. This is another use case of AIOps. Companies are developing tools to help map out systems so you can see the impact of code changes and updates.

Tools like Epsagon can help automatically detect system dependencies, providing your software team with a clear understanding of where their systems are going and which systems they need to monitor when migrating and updating systems that interact with each other.

These are three great use cases where AIOps fits into your IT strategy. Overall, there is a very distinct difference between MLOps vs AIOps.

* * * * *

MLOps vs. AIOps
===============

The term 'AIOps' is often used interchangeably with 'MLOps,' which is quite incorrect.

AIOps is all about supporting and reacting to its issues in real-time and providing analytics to your operations teams. These functions include performance monitoring, event analysis, correlation, and IT automation. According to Gartner, AIOps combines big data and machine learning to automate IT operations processes. Therefore, the end goal of AIOps is to automatically spot issues in day-to-day IT operations and proactively react to them using Artificial Intelligence. In fact, research shows that [21% of organizations are planning to adopt AIOps](https://thenewstack.io/the-current-state-of-aiops/) within a year!

MLOps, on the other hand, focuses on managing training and testing data that is needed to create machine learning models effectively. It is all about monitoring and management of ML models. It focuses on the Machine Learning operationalization pipeline. AIOps is all about applying cognitive computing techniques to improve IT operations, but it is not to be confused with MLOps.

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So Yes, AIOps and MLOps Are Very Different
==========================================

Coupled with the increasing complexity of architectures for our modern applications, the demands of this digital economy have made the role of IT operations much more challenging. As a result, ML and AI have emerged to automate some manual business processes and offer efficiency.

MLOps or AIOps both aim to serve the same end goal; i.e. business automation. While MLOps bridges the gap between model building and deployment, AIOps focuses on determining and reacting to issues in IT operations in real-time so as to manage risks independently.

We are currently in the golden age of AI. However, successful implementation of these operations requires training and investment. Therefore, if you wish to stay relevant *and *ahead in the competition of the current day's industry, it is crucial for you to understand and adopt these technologies.

If you liked this content, then here are some more articles on data science and big data.

[4 SQL Tips For Data Scientists](https://medium.com/better-programming/4-sql-tips-for-data-scientist-and-data-engineers-56c41487752f?source=friends_link&sk=791304065e2299e675ca1bb10bed197b)

[What Is Dask and How Can It Help You as a Data Scientist?](https://www.theseattledataguy.com/what-is-dask-and-how-can-it-help-you-as-a-data-scientist/)

[Data Engineering 101: An Introduction To Data Engineering](https://www.theseattledataguy.com/data-engineering-101/)

[5 Great Libraries To Manage Big Data With Python](https://www.theseattledataguy.com/5-great-libraries-manage-big-data-with-python/)

[How To Write Better SQL Episode 2](https://www.youtube.com/watch?v=i4VTKQZuuQw&t=10s)