Growing concerns to manage complex business operations and proliferation of the data generation is expected to drive the global ML Ops market for the forecast period.
According to TechSci Research report, “ML Ops Market - Global Industry Size, Share, Trends, Opportunity and Forecast, 2016-2026 Segmented By Solutions (Data Management, Modelling, Continuous Deployment, Computing and Resource), By Product Focus (Data-Centric, Model Centric), By Task (Model Lifecycle Management, Model Versioning & Iteration, Model Monitoring & Management, Model Governance, Model Security), By Component (Platform, Services (Professional, Managed)), By Type (Public Cloud, Private Cloud, Hybrid Cloud), By Organization Size (Large Enterprises, Small & Medium Sized Enterprises), By End Use (BFSI, IT & Telecom, Retail, Manufacturing, Public Sector, Others) and By Region”, the global ML Ops market is expected to grow at a rate of steady CAGR for the forecast period, 2022-2026. ML Ops stands for machine learning operations and makes the use of machine learning models by the development/operations team. The main objective of ML Ops is to manage the deployment and development of machine learning models by stating the process to make machine learning more dependable and productive.
The development of the machine learning model is different from the conventional application development as the machine learning (ML) models rely upon the data, including the training data, test data, real-time data, and validation data. Organizations actively adopt ML Ops to efficiently manage the ML models and handle the range of ML-model specific management needs. ML Ops manages the process required for the model creation and monitors the data used at the training and for real-time applications.
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Global ML Ops market is segmented into solutions, product focus, task, component, type, organization size, end use, regional distribution, and company. Based on task, the market is divided into model lifecycle management, model versioning & iteration, model monitoring & management, model governance, and model security. The ML Ops tools are required to manage the model lifecycle, training, deployment, and operationalization. ML Ops provides a reliable process to move models from data science management to production management. Model versioning & iteration helps in iteration and versioning of the machine learning models to deal with new requirements as the ML models can change based on the real-world data.
Model monitoring and management aids in monitoring and managing the model usage, consumption and check the accuracy and performance of the generated results. Model governance helps in model access control, enhancing transparency in the working of the ML models, and model security is used to provide protection to model from security threats, cyber-attacks and prevent the access of unauthorized users. Based on type, the market is divided into public cloud, private cloud, and hybrid cloud. The hybrid cloud is expected to witness the fastest incremental growth for the next five years. The growing adoption of the hybrid cloud model by enterprises for big data processing, enhanced flexibility, and scalability influences the market demand. Hybrid cloud also provides increased security and can efficiently optimize the workload resources.
Microsoft Corporation, Amazon Web Services, Inc., Google, LLC, IBM Corporation, Dataiku SAS, Iguazio Ltd, Databricks Inc., DataRobot, Inc., Cloudera, Inc., Modzy, Algorithmia, Inc., HP Enterprises Co., Valohai, Allegro AI Ltd., Comet ML Inc. are the leading players operating in global ML Ops market. Service providers are increasingly focusing on research and development process to fuel higher growth in the market.
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“The ongoing technological advancements and the adoption of advanced technologies by the enterprises to provide an enhanced experience to the consumers and boost the profit margin is generating the need for advanced software and services. Machine learning helps businesses deploy solutions, save cost, optimize workflow, and use data analytics technology to make smart decisions. ML Ops is used for the deployment of machine learning model to lower the operational costs and minimize time. High-end investments by the market players and the growing IT sector is expected to propel the global ML Ops market growth till 2026” said Mr. Karan Chechi, Research Director with TechSci Research, a research based global management consulting firm.
“ML Ops Market - Global Industry Size, Share, Trends, Opportunity and Forecast, 2016-2026 Segmented By Solutions (Data Management, Modelling, Continuous Deployment, Computing and Resource), By Product Focus (Data-Centric, Model Centric), By Task (Model Lifecycle Management, Model Versioning & Iteration, Model Monitoring & Management, Model Governance, Model Security), By Component (Platform, Services (Professional, Managed)), By Type (Public Cloud, Private Cloud, Hybrid Cloud), By Organization Size (Large Enterprises, Small & Medium Sized Enterprises), By End Use (BFSI, IT & Telecom, Retail, Manufacturing, Public Sector, Others) and By Region” has evaluated the future growth potential of global ML Ops market and provided statistics & information on market size, shares, structure and future market growth. The report intends to provide cutting-edge market intelligence and help decision makers take sound investment decisions. Besides, the report also identifies and analyzes the emerging trends along with essential drivers, challenges, and opportunities in the of global ML Ops market.
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