The global MLOps (Machine Learning Operations) market, solutions that help data scientists simplify and optimize machine learning deployment processes, it will have an average annual growth of almost 45% until 2030. The projection was made by the research company Valuates Reports, which estimates a jump in the valuation of the segment of US$ 186,4 million, achieved in 2023, for US$ 3.6 bi. One of the main reasons for the heating of this market may be the reduction in the time required for the development of predictive models. The evaluation is by Carlos Relvas, Chief Data Scientist at Datarisk, company specialized in the use of artificial intelligence to generate value in the concept "decision as a service
According to him, to develop similar systems using traditional methods, organizations take an average of two to three weeks, depending on the complexity of the sector.
"In contrast", by using MLOps, the data scientist can automate the entire creation process. First, he does all the model training through an automatic machine learning that tests algorithms to see which one works best. At this moment, the scientist can also, if you want, upload a code that he already has and save all documents and all codes, thus ensuring the protection of the documentation of all databases. The success of MLOps is due to the fact that it eliminates all these steps with the model creator being responsible and having everything they need to go from the beginning to the end of the project, affirms
In 2024, Datarisk launched a MLOps solution aimed at serving leading companies in activities such as credit granting, risk of fraud, propensity to change jobs, productivity in agriculture, among others. Only during the first half of this year, the tool was used to carry out a volume of over 10 million queries and, among the benefits obtained by users of this technology, one of the biggest highlights was precisely the reduction of time. With the MLOps of the startup, the average period of three weeks has dropped to a matter of hours
Carlos Relvas also explains that, after this first training is built, a second stage enters within the MLOps platform of Datarisk which is the part where the scientist can automatically, he himself, create an API for the model to be used in external environments. The third stage, according to him, it is the solution management. At this stage, the goal is to ensure that this model that was developed, trained and is being used continue having good performance over time. The tool can monitor both the usage of your applications and the functioning of the APIs to ensure not only that everything is operating as planned, but also allow for the assessment of the model's quality. The solution enables verification, for example, if there is any variable that has changed over time and issues alerts to the end user if the model is losing performance, affirms
The market receptiveness and the prospects that Datarisk has been making allow the company to project a growth of more than five times the volume of use of this solution by the end of 2025
The Co-founder and CEO of Datarisk, Jhonata Emerick, explains that by becoming a pioneer in offering solutions in the MLOps concept in Brazil, the startup is implementing the strategy of maturing and refining its main business theses. "We understand the market's needs more deeply and are now prepared to offer solutions capable of transforming the reality of data science in the country in an absolutely relevant way", say
According to Emerick, in the specific case of developing predictive models, MLOps solutions emerge as a response to slow internal processes designed for a time when companies did not need to manage a data area with the agility that is currently required
"IT queue systems are generally adopted in which the data science area finishes creating a model and hands it over to the engineering area to create an API". This, in turn, it will take a significant amount of time to do your part, when will the project then be passed to the credit engine team, for example, so that he finally implements this API, what will lead to other deadlines. The result is that, when the model is implemented, the situation is already different. That is why the MLOps solution becomes so effective in terms of optimization, concludes
