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What is MLOps and why should you use it?

MLOps is a novelty that is gaining prominence and is related to Big Data. Its literal translation would be “Machine Learning Operations”, that is, machine learning operations that some teams can carry out. But, since there is a lot of talk about this today, you should know more about what it is and how to take advantage of it.

What is MLOps?

MLOps is a part of what is known as DevOps which, as you will surely remember, is a series of actions whose mission is the development of high quality software. Well, this has wanted to go a step further by also offering companies a series of practices so that they can use Artificial Intelligence in their favor, making their teams or operations learn automatically.

What is sought when implementing MLOps in a company is to streamline efforts. Achieving faster development on a machine learning model you’re working on, reducing risk, and enjoying greater efficiency is what MLOps delivers. This is why you should start using it in your own organization, although you may need more information about it.

Why do you need MLOps?

You need MLOps because in a world where data is so important, knowing how to manage it effectively, betting on agile processes, is essential. Also, if you have noticed a slowdown in the implementation of machine learning models in your company, for example, with MLOps you will notice that there is a big difference. Its implementation will be faster.

Also, you need MLOps because the deliveries of the models will be more accelerated, without long waits. This will be a strong value bet by your company that will enjoy a competitive advantage over the others. However, since the risks are reduced, your time and money will be protected. Well, there will be models that will have to be discarded and it is best that you know this as soon as possible.

MLOps machine learning also helps models to be constantly monitored. This translates into greater efficiency of the data that can be obtained and that changes over time. Do not forget that these evolve and it is essential, to maintain accuracy, that you have MLOps in your organization. A resource that will keep the model up to date.

How is an MLOps process carried out?

Although what has been said so far may seem quite simplistic, the truth is that an MLOps process is not that simple. It requires a series of steps that you should know so that this tool can be useful to you. To do this, we will explain what you have to do from the beginning to the end so that machine learning begins to be part of your company and gives you all its advantages:

  • Creation of the model and tests, which will imply that you have to prepare the data. It is very important that you carry out an exhaustive analysis of which ones can be useful to you and which ones are not. Focusing on your quality is crucial.
  • Requirements adjustment. You have to define what are the needs that you are going to have, how will be the control of the versions and the administration of the different models. This should be clear at this point.
  • Define operations such as the extraction of the data, the automations that will be carried out, how the model will be implemented or how it will be integrated into certain applications. This is also crucial.
  • Continuous monitoring to check how the data is evolving, make the necessary changes and ensure that the performance is as expected. All this is part of the final process of the MLOps.

The advantages of MLOps for your organization

MLOps has several advantages, among which the creation of reusable software environments in which existing models can be trained and improved for your benefit. Likewise, it is worth insisting on the possibility of accelerating its creation time, which will optimize the productivity of your company.

Besides all this, there are no limits to the number of models you seek to integrate, monitor or implement. This is great to enjoy the improvement of various models that you are working on in your company and that will end up giving good results in systems that work with Artificial Intelligence. Thus, you will be able to carry out several tests at the same time without the figures being a cause for concern.

MLOps offers you greater value when creating models, validating them and obtaining simplified solutions for complex tasks. Therefore, it is a trend that is not surprising that it is becoming more and more prominent, because its presence in your business can make it scale to another level. Integrating machine learning applications is easier thanks to MLOps.

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