MLOps (Machine Learning Operations) Machine Learning Methodology

DevOps Methodology

Main article: DevOps Methodology

Machine Learning

Main article: Machine Learning

Chronicle

2024: MLOps Global Instrument Market Size Reaches $1.58 Billion for the Year

At the end of 2024, costs in the global MLOps instrument market reached $1.58 billion. More than a third of this amount was provided by the North American region. Such data are provided in a Fortune Business Insights study, the results of which were published on April 7, 2025.

MLOps, or Machine Learning Operations, is a set of practices that automate and simplify the development, deployment and operation of machine learning. Companies can use MLOps to optimize and standardize processes throughout the machine learning lifecycle. These processes include model development, testing, integration, launch, and infrastructure management. The modern MLOps ecosystem brings together many tools - from cloud platforms to open solutions. Each tool performs certain functions in the process of creating and supporting machine learning systems.

The implementation of MLOps provides organizations with a number of significant benefits. First of all, this is a significant acceleration in the launch of models to market: automated processes shorten the path from idea to industrial implementation. In addition, MLOps frees specialists from routine tasks, which allows you to increase the efficiency of teams. Automatic testing and monitoring ensures the stability of the models in operation. MLOps practices improve troubleshooting and model management in production. In particular, software developers can track system performance and reproduce their behavior to solve problems.

But there are also certain deterrents, one of which is security-related issues. Organizations need to protect sensitive data, secure access to models and infrastructure, and compliance. In addition, it is important to have a structured process for validating, evaluating, and approving models before they are put into effect. This may include checking for fairness, bias, etc. Analysts stress that working with legacy libraries is one of the most common challenges facing businesses. According to IBM's report on the use of artificial intelligence, about one in five companies claim difficulties in securing data.

According to the deployment method, the market is divided into cloud, local and hybrid segments. Cloud platforms offer end-to-end solutions for the entire machine learning cycle. However, the hybrid segment dominates, with security and cost concerns prompting most companies to adopt a balanced approach that combines cloud and on-premises data centers. In terms of the use of MLOps, IT and telecommunications, healthcare, BFSI (banking, financial services and insurance), production, retail, etc. The first three areas provided a significant contribution in 2024: for example, the share of IT was 27.6%. From a geographical point of view, North America leads with costs of $0.57 billion. Globally, the following are named major players:

• DataRobot;
• Domino Data Lab;
• AWS;
• Microsoft;
• IBM;
• NPE;
• Allegro AI.;
• Mlflow;
• Google;
• Cloudera.

Fortune Business Insights analysts believe that in the future, the CAGR in the market under consideration will be 35.5%. As a result, by 2032, costs could increase to $19.55 billion.^[1]

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