It doesn’t do that long to learn the software development life cycle there were hardly any complications. But with the arrival and subsequent expansion of Artificial Intelligence, and all kinds of new technologies to the company, including machine learning, it became necessary review the cycle. Before, we went from design to development, to continue later with the development of the application in question, and finish with the testing of what was developed and its deployment if it was correct, to go on to its periodic maintenance. It was an iterative model, which returned to the beginning when it was necessary to replace the software used with an improved or different one. But these new technologies complicated everything.
We are now at a point where a large number of companies are in the process of integration of Artificial Intelligence and machine learning to your products. In the second case, it is therefore necessary to integrate this technology into the process, and modify this software development life cycle in some way.
For this reason, in many cases in which it is necessary to develop software that is powered by machine learning, the MLOps: Machine Learning Operationsalso know as ModelOps.
What is MLOps?
The definition of MLOps can be extracted from what we have discussed so far: it is a discipline that tries to unify the development of machine learning systems, and its deployment, with the aim of standardizing and optimizing the continuous delivery of high-performance models that are in production. All this by “borrowing” DevOps principles, but instead of achieving application development through an iterative approach as in DevOps, what is achieved with MLOps is, using the same principles, to bring machine learning models to production.
MLOps can also be defined as an infrastructure that supports a continuous learning system, based on a branch of Artificial Intelligence, and used with various processes and methods. It requires both expert professionals and specific tools, and it covers three aspects of machine learning: design, training, and deployment. But why is it necessary to adopt this development system in many application creation processes?
Well, neither more nor less, because the large amount of data with which applications are forced to work, and those who use them, make it necessary to provide decision and process automation capabilities to a multitude of programs and tools. This creates many complications, which cannot be effectively addressed through conventional application development systems.
The life cycle of machine learning systems
But to understand how MLOps works, you first have to understand how the life cycle of machine learning systems. Often observed by companies that have embedded data into their processes, or use data on a regular basis, this cycle takes into account the need to work with several different teams.
In general, these teams are the following: Business or product development, which defines the business objectives and the points of interest to be taken into account; Data engineering, which is responsible for obtaining and preparing the data; Data science, whose members are responsible for generating the architecture of machine learning solutions, as well as the development of models; and IT or DevOps, the team that complements the configuration of the deployment and controls it together with the data scientists.
With the intervention of these teams, the machine learning life cycle would begin with the identification of machine learning problems in development based on business objectives. Once these have been identified, it is time to access the data necessary to feed the model, as well as to prepare and process it.
With them ready, it is time to create the architecture of the machine learning solution to be used, as well as to develop the machine learning models. These models are then deployed when they are ready, and the iteration phase of the machine learning lifecycle begins: controlling, optimizing, and maintaining model performance.
Solutions developed using MLOps make it possible to collect, analyze and interpret large amounts of data. From this, and using various machine learning algorithms, these solutions can identify and detect failures and anomalies in infrastructure or applications that generate performance problems.
In fact, the goal of MLOps is to use these algorithms to automate repetitive tasks found in data science projects, and use historical analytics results for more efficient and streamlined operations. And by using deep learning algorithms capable of processing large-scale data, it achieves much better results than conventional business intelligence applications.
Advantages of using MLOps
With the use of MLOps, companies gain efficiency and achieve objectives related to data-driven analysis. In addition, this system also allows you to improve intelligence regarding business operations. This ensures that those who have to make decisions have more information to be able to make the most effective ones.
Furthermore, solutions developed using MLOps also make it easy and flexible to deploy models in any of the nodes of a company network. In this way, work teams, which are usually made up of experts in various development and data science disciplines, can use the company’s infrastructure to test models, and also to train them.
Thanks to MLOps it is also possible to process iterations are easier, as it allows to offer pre-built workflows that can be used with various common tasks in machine learning and artificial intelligence. For example, in model training and validation, as well as in deployments in varied environments.
MLOps solutions can also mitigating the risk involved in the use of models of machine learning. To do this, tools are used to control that the models perform adequately over time. Thus, companies can decide if a specific model needs to be updated or replaced with a new one, or with a new version, or if it is not necessary to do so.
On the other hand, companies that have to manage sensitive customer data, which requires them to comply with very strict regulations related to data privacy, can use MLOps through solutions designed specifically for their compliance. By doing so, machine learning models can be tested and trained without risk of exposing the information to be protected.
Developers can, with automated MLOps solutions, develop, train, and test machine learning models much faster than doing it by hand or with more rudimentary systems. And the deployment of these MLOps solutions is also fast: it takes only a few days or a few weeks.
All this, together, allows developers to achieve results faster, and can improve models with less effort and time. The result? Lower costs and less human workwhich allows the efforts of the members of the development teams to be devoted to other tasks or projects.
But not only that: when there is a security breach, MLOps solutions can be used to identify threats and prevent future attacks. They do this because they have learned how they occur, and can identify similar events in real time, that is, when the attack occurs.
In addition, with the appropriate adjustments and developments, they can be used to identify damage produced in an attack, which facilitates the warning to those affected and the damage mitigation. As you can see, the advantages of using MLOps in the development of systems with machine learning are multiple, so its adoption in companies that are adopting this technology and Artificial Intelligence in the systems they develop is being quite fast.
