The MLOps market size is expected to advance at a CAGR of 39.3% during 2022–2030, to reach USD 17,335 million by 2030.
Machine learning is focused on the usage of data and algorithms to make the computers learn the way humans do through past experiences. ML improves its accuracy as it gains knowledge by being exposed to more information and scenarios. The data is stored in diverse formats – structured and unstructured, text, video files, and images. To make the data capable of analysis, it is required to be cleaned and structured. For the better utilization of data, the demand for operationalization of ML is required to structure it.
The aim of implementing MLOps is to accomplish and accelerate the lifespan of analytics and ML models from the development stage to production, and then maintain and monitor them. It requires automation and monitoring at all steps of ML model engineering that includes incorporation, testing, releasing, deployment, and infrastructure management. This also makes it easier for machine learning engineers, data scientists, and other AI practitioners to have comprehensive visibility over projects and initiatives.
Moreover, MLOps allows developers, IT operations teams, quality testing teams, security analysts, and other business users to use real-time data analysis from multiple sources. Also, it assists in making decisions throughout each stage of the application lifecycle. Automating regular tasks that require skilled resources further enables businesses to tap into untapped human potential.
Some major industries that incorporate AI in their operational processes include healthcare, retail and e-commerce, food, BFSI, logistics and transportation, and gaming. The importance and bourgeoning applications of AI in healthcare are one of the key factors fuelling the market for MLOps. The automation technology allows physicians and practitioners to access the database comprising a large number of diagnostic resources and form insights based on stored data.
For instance, outcomes of the diagnosis of a disease may show multiple symptoms that correlate with various other conditions by physical and genetic characteristics, which can delay or interrupt the diagnosis process. In this, AI helps physicians with providing numerical and qualitative data based on input feedback. Thus, it aids in terms of efficiency by enhancing accuracy in early detection, treatment plan, and outcome prediction of the disease.
Furthermore, more than half of the companies are concentrating on data engineers as their primary professionals. These engineers primarily work to shape systems that accumulate, manage, and transform raw information and statistics into operational information for data scientists and business analysts to interpret. Therefore, MLOps models and platforms are highly used by such engineers. Also, large tech giants sell their ML solutions to data engineers. Their eventual goal is to make the gathered facts and figures manageable so that organizations can use them to assess and augment their performance.
In addition, ML engineers are the second-largest users of machine learning operationalization models and accounts for a significant market share in 2022 and is expected to grow at a CAGR of around 39.7% during the forecasted period, as they focus on procedures and management of ML models, algorithms, and processes. Also, they work with data scientists to support them to use projects effectively and regularly monitor the working of the models they created.
The major MLOps market players are Akira AI, Alteryx Inc., Amazon Web Services Inc., Cloudera Inc., DataRobot Inc., Domino Data Lab Inc., Gavs Technologies, Google LLC, H2O.ai Inc., Hewlett-Packard Enterprise (HPE), IBM Corporation, and Microsoft Corporation.