Welcome to the world of DevOps and MLOps!
In simple words, DevOps (short for development operations) is a method that combines both development and IT operations. It is a combination of DevOps tools and actions that will increase any organization’s ability to deliver results and services faster as compared to the traditional alternatives.
MLOps (short for machine learning operations) is an extension of DevOps and it refers to the use and development of machine learning models by the DevOps team.
Following are the sub-services of DevOps and MLOps:
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Model Deployment and Management:
Model deployment refers to the entire process of putting machine learning models into production. This gives access to the model’s predictions for the users and developers. This allows them to make accurate business decisions based on the data. -
Infrastructure Automation:
This involves the use of technology that performs tasks with less human assistance to control data storage components used to deliver IT technology and solutions. Besides this infrastructure automation also controls the hardware, software, and operating system.
How can DevOps and MLOps benefit your organization?
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DevOps has some amazing technical benefits for your organization. Firstly, it offers faster resolution of issues and rapid product delivery. It enables better resource utilization which will increase your organization’s profitability. Moreover, it leads to greater scalability, automation, innovation, and increased visibility into outcomes.
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MLOps bring many benefits and the central ones are efficiency, scalability, and reduced risks. Since data teams can achieve model development faster they can deliver machine learning models in higher quality leading to faster production. This will inevitably increase efficiency for your organization. Scalability and management will be enabled as vast numbers of models can be managed and controlled continuously. Since machine learning models require continuous scrutiny, MLOps help cater to such requests. This will ensure an organization’s policies are complied with.