Global MLOps Technology Market Growth (Status and Outlook) 2023-2029
MLOps is the process of taking an experimental Machine Learning model into a production system. The word is a compound of “Machine Learning” and the continuous development practice of DevOps in the software field. Machine Learning models are tested and developed in isolated experimental systems. When an algorithm is ready to be launched, MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems. Similar to DevOps or DataOps approaches, MLOps seeks to increase automation and improve the quality of production models, while also focusing on business and regulatory requirements. While MLOps started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. MLOps applies to the entire lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.
According to this study, the global MLOps Technology market size will reach US$ million by 2029.
This report presents a comprehensive overview, market shares, and growth opportunities of MLOps Technology market by product type, application, key players and key regions and countries.
Segmentation by product type:
On-premise
Cloud
Hybrid
Segmentation by Application:
BFSI
Healthcare
Retail
Manufacturing
Public Sector
Others
This report also splits the market by region:
United States
China
Europe
Other regions:
Japan
South Korea
Southeast Asia
Rest of world
The report also presents the market competition landscape and a corresponding detailed analysis of the major players in the market. The key players covered in this report:
Microsoft
Amazon
Google
IBM
Dataiku
Lguazio
Databricks
DataRobot, Inc.
Cloudera
Modzy
Algorithmia
HPE
Valohai
Allegro AI
Comet
FloydHub
Paperpace
Cnvrg.io
Please note: The report will take approximately 2 business days to prepare and deliver.
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