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MLOps vs DevOps
It would have often occurred that the development team has moved on to a new project while the operations team provides feedback on the previous one. This caused the deadline to be pushed back, for the entire software development cycle or machine learning model development cycle. For this reason, IT has adopted the new ways of working for preparing software and machine learning models, they are MLOps and DevOps. In this blog, you will get to know about these terms and how they differ.
What is DevOps?
The term DevOps stands for Development + OperationS. It is a method in which people collaborate to build and deliver software as quickly as feasible. DevOps enables software development (Dev) and operations (Ops) teams to collaborate and iteratively accelerate the delivery of software. The DevOps methodology aids in improving communication between developers and operations personnel working on projects. It isn't simply one tool or method that can get the work done; rather, it is a strategy that provides for greater flexibility when it comes to operational adjustments.
What is MLOps?
The term MLOps stands for Machine Learning + OperationS. It is a business model created by machine learning companies. MLOps is a concept that removes traditional vertical silos in enterprises, allowing them to share resources and expertise across departments. MLOps is a collaboration and communication platform for data scientists and operations experts to manage the production ML lifecycle. Companies cannot gain real value from AI unless they can automate the deployment of machine learning models in production and use specialized and automated capabilities to monitor, manage, and govern them.
Similarities between MLOps and DevOps
Following are the similarities between MLOps and DevOps −
It's all about simplifying procedures in DevOps and MLOps. The development, testing, and operational components of software development are all brought together in DevOps. MLOps, on the other hand, are approaches for streamlining the machine learning life cycle from start to finish. It intends to reduce ML development turnaround times by bridging the gap between design, model development, and operations.
Developers, data scientists, and data engineers are all working together on the same code base. They make continuous integration and development. This CI/CD procedure is followed by both MLOps and DevOps.
Communication is at the heart of DevOps and MLOps. DevOps relies on various departments and a set of technologies that help in visibly facilitating the processes, so clear communication is critical for process automation, continuous delivery, and continuous feedback. Similarly, in MLOps, communication provides the foundation for collaboration between system administrators, data scientists, and other departments across the company, resulting in the shared knowledge of how production models are built and maintained.
Key Differences between MLOps and DevOps
Following are the key differences between MLOps and DevOps −
Dealing with data in machine learning is a difficulty in and of itself. A neural network, for example, requires a large data collection, which entails long-running jobs. With DevOps, this is not the case.
Software developers and DevOps engineers are commonly involved in DevOps, whereas Data scientists and Machine Learning Engineers are primarily needed in MLOps.
It's normal for data scientists working on machine learning projects to try one technique to solve a problem and then test another a few days later. Over a few weeks or months, you might experiment with numerous. Traditional software engineering also requires some experimentation, although it is usually brief and done apart from the main project.
Additional data and model phases are included in the MLOps pipeline to build/train a machine learning model. Additional data and models are not required in DevOps.
DevOps focuses on developing a generic application and employs a standard library set for certain use scenarios. MLOps, on the other hand, creates a model that feeds inferences and covers a wide range of languages, tools, libraries, and frameworks.
Code version control is used in DevOps to ensure that any changes or updates to the product being created are documented clearly. The code, on the other hand, isn't the only variable in machine learning. Data, as well as parameters, metadata, logs, and the model, are all crucial inputs that must be managed.
There is a third idea in MLOps that does not present in DevOps − Continuous Training (CT). This step is all about automatically detecting scenarios/events that necessitate retraining and redeploying a model into production due to performance degradation in the existing deployed machine learning model/system.
In DevOps, code generates an application, which is subsequently turned into an executable and deployed after being validated with a large number of test cases. In MLOps, however, code is used to create or train a machine learning model. Validation is used to determine the model's correctness, or how well it performs with test data. This step is repeated until the model gains a certain level of great performance.
Conclusion
Today, no successful software firm can function without using DevOps ideas and tools. Similarly, without some shared MLOps principles and tooling, it will be impossible to manage the development and productization of machine learning models in the future. These two terms have been advancing the IT industry to provide efficient software and models for many years now and will be continued in the future.
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