Chicago, April 21, 2023 (GLOBE NEWSWIRE) -- The MLOps Market size is projected to grow from USD 1.1 billion in 2022 to USD 5.9 billion by 2027, at a CAGR of 41.0% during the forecast period, according to a new report by MarketsandMarkets™.
Monitorability and Scalability has fueled the demand of MLOps. Moreover, standardizing ML processes for effective teamwork is expected to drive the market growth for MLOps Market. MLOps help companies save time and reduce error rates. Collaboration is seen between IT and business personnel, as well as data scientists and engineers, for the company-wide adoption of ML models.
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188 - Tables
45 - Figures
219 - Pages
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Scope of the Report
| Report Metrics | Details |
| Market size available for years | 2018–2027 |
| Base year considered | 2021 |
| Forecast period | 2022–2027 |
| Forecast units | Value (USD) Million/Billion |
| Segments covered | Component, Deployment mode, Organization size, Vertical |
| Region covered | North America, Europe, Asia Pacific, Middle East and Africa, and Latin America |
| Companies covered | Major Vendors - IBM (US), Microsoft (US), Google (US), AWS (US), HPE (US), GAVS Technologies (US), DataRobot (US), Cloudera (US), and Alteryx (US) Startup/SME Vendors - Domino Data Lab (US), Valohai (US), H2O.ai (US), MLflow (Netherlands), Neptune.ai (Europe), Comet (US), SparkCognition (US), Hopsworks (Europe), Datatron (US), Weights & Biases (US), Katonic.ai (Australia), Modzy (US), Iguazio (Israel), Teliolabs (US), ClearML (Israel), Akira.AI (India), and Blaize (US). |
AI/ML is regarded as more important than other technologies. According to a 2020 Gartner poll of over 200 organizations, 66 percent of corporations did not modify their AI investments throughout the pandemic, while 30 percent chose to raise their AI finance during the 2020 epidemic. The AI/ML models require necessitates continuous monitoring, experimentation, correction, and retraining of AI models. All of the process is time-consuming and costly in development and production models. To effectively implement MLOps, enterprises are required to develop a number of core capabilities, including full lifecycle tracking, metadata optimized for model training, hyperparameter logging, and a solid infrastructure. MLOps provides a considerably more efficient development pipeline that can rapidly turn mistakes into successes along with cutting overall costs.
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Based on deployment mode, cloud segment is expected to grow with the highest CAGR during the forecast period. The advantage of cloud deployment of these solutions is the reduced physical infrastructure requirement, which results in low maintenance costs for organizations. Most ML experiments begin with analyzing the data on and do not necessitate a large amount of processing resources. Organizations will shortly find themselves in need of more resources than their own servers can provide. By far the most scalable environment for ML models is the cloud. Cloud-based MLOps platforms are an excellent option for one-time events and occasional content services. The cloud providers like AWS, Google Cloud Platform, and Microsoft Azure can offer lower total cost of ownership while providing superior features ranging from scalability to security.
The MLOps market is expected to register a higher growth rate in Asia Pacific (APAC). MLOps platforms have witnessed a wide-scale adoption across various industry verticals. AI spending In APAC region is rapidly increasing. The investment comes initially from the banking industry, where financing for better AI will aid in the reduction of fraud and risk. This could lead APAC into leading position as global corporations determine where to direct their fundings. The region’s financial institutions would be able to construct new AI/ML models better and faster than other organizations around the world owing to APAC’s alternative, unstructured and supply chain data advantages.
Market Players
The major players in the MLOps market are IBM (US), Microsoft (US), Google (US), AWS (US), HPE (US), GAVS Technologies (US), DataRobot (US), Cloudera (US), Alteryx (US), Domino Data Lab (US), Valohai (US), H2O.ai (US), MLflow (Netherlands), Neptune.ai (Europe), Comet (US), SparkCognition (US), Hopsworks (Europe), Datatron (US), Weights & Biases (US), Katonic.ai (Australia), Modzy (US), Iguazio (Israel), Teliolabs (US), ClearML (Israel), Akira.AI (India), and Blaize (US). These players have adopted various growth strategies, such as partnerships, agreements and collaborations, new product launches and product enhancements, and acquisitions, to expand their footprint in the MLOps market.
Frequently Asked Questions (FAQ)
How is the MLOps market expected to grow in the next five years?
According to MarketsandMarkets, the MLOps market size is projected to grow from USD 1.1 billion in 2022 to USD 5.9 billion by 2027, at a CAGR of 41.0% during the forecast period.
Which region has the largest market share in the MLOps market?
North America is estimated to hold the largest market share in MLOps market in 2022. North America is one of the technologically advanced markets in the world.
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