MLOps Market worth $5.9 billion by 2027, growing at a CAGR of 41.0%: Report by MarketsandMarkets™

Chicago, Aug. 17, 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
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219 - Pages

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Scope of the Report

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.

Top Trends in MLOps Market

• The goal of MLOps was to fully automate the machine learning lifecycle, from model training and data preprocessing to deployment and monitoring. Processes were streamlined, errors were decreased, and overall productivity increased because to automation.
• By focusing on cooperation between data scientists, developers, and operations teams, MLOps adopted DevOps ideas. By bridging the gap between model creation and deployment, this collaboration enabled quicker and more reliable deployments.
• Version management and tracking for machine learning models and the data they are connected with were the main focuses of MLOps tools. This made it possible for teams to monitor model modifications, replicate studies, and guarantee reproducibility.
• The integration of CI/CD practises into MLOps workflows allowed for the quick and automatic deployment of machine learning models. This facilitated the more regular delivery of model updates and upgrades by organisations.
• The requirement for comprehending and interpreting AI models' judgements grew as they grew more complicated. To increase transparency, MLOps used techniques and tools for model interpretability and explainability.

Key Industry Development

• As more businesses realise the value of operationalizing machine learning models, the MLOps market has continued to develop. Across several sectors, including finance and healthcare as well as retail and manufacturing, adoption rates have risen.
• Industry best practises and standards are beginning to emerge as the MLOps area develops. The establishment of standardised processes for model development, deployment, monitoring, and maintenance is a top priority for organisations.
• Leading cloud providers and sellers of AI/ML platforms have built MLOps functionality right into their platforms. The end-to-end solutions that this integration promises to offer will accelerate the adoption of MLOps practises and simplify the machine learning lifecycle.
• The management of a wide variety of machine learning models across numerous departments and use cases is a priority for large businesses, which is why they are investing in complete MLOps solutions. Centralised governance, security, and collaboration features are frequently provided by these solutions.
• Version control systems created specifically for machine learning models and data have developed as collaboration tools for MLOps. These technologies are designed to make it easier for cross-functional teams to collaborate.

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