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Home / Blog / MLOps / What Differs Between MLOps Engineers & DevOps?
Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of AiSPRY and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.
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At this moment, practically every other company is attempting to integrate AI/ML into its product. The new requirement of creating ML systems adds to or modifies some SDLC concepts, making a new engineering field known as MLOps.
A new term called "MLOps" has emerged, generating excitement and spawning new job profiles. Machine Learning Operations, or Model Ops as sometimes called, is abbreviated as MLOps. Software development and IT teams' processes can be automated and integrated using the practices, tools, and cultural philosophy called "DevOps." It strongly emphasizes team empowerment, interterm coordination, and technological automation.
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The standard software development methodology, in which developers generated code apart from operations, which distributed and supported the code, raised concerns in the IT operations and software development communities about 2007. The DevOps movement was thus established as a result. DevOps, which combines the terms "development" and "operations," refers to combining various disciplines into a single, ongoing activity.
An IT operations team and a development team work together throughout the product lifecycle to increase the speed and quality of software deployment. As a result, this new way of working and cultural change will greatly impact groups and the companies they work for.
Under a DevOps strategy, development and operations teams are no longer "siloed." Instead, these two teams can occasionally create a single group of engineers who work across the whole application lifecycle, from development and testing to deployment and operations, and have various multidisciplinary skills.
DevOps teams employ tools to automate and accelerate processes, which improves reliability. With the help of a DevOps toolchain, teams may also take on essential DevOps components like continuous integration, continuous delivery, automation, and communication.
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DevOps values are occasionally applied by teams outside of development. When security teams apply a DevOps paradigm, security becomes an active and integrated part of the development process. It is called DevOps.
We used to work on tiny scales with manageable amounts of data and a relatively small number of models. Now that we are integrating decision automation into various applications, numerous technical difficulties arise when developing and implementing ML-based systems.
One must first understand the lifecycle of ML systems to comprehend MLOps. A data-driven organization's lifecycle involves multiple different teams. The teams that contribute from top to bottom are as follows:
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As DevOps has evolved, so has its complexity. Two factors drive this complexity:
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The stage at which MLOps principles are being used can be used to distinguish the best practices for MLOps.
In machine learning initiatives, the scope of MLOps can be as narrow or broad as the project requires. In some circumstances, MLOps can include everything from the data pipeline to model production, whilst other projects would only need the model deployment procedure to be implemented using MLOps. The majority of businesses use the following when applying MLOps principles:
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A set of engineering methods known as MLOps, which are particular to machine learning projects, are based on the more popular DevOps principles in software engineering. MLOps adopts the same concepts to put machine learning models into production whereas DevOps uses a quick, continuous iterative approach to releasing apps. Higher software quality, quicker patches and releases, and more customer happiness are the results in both situations.
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