In this article, we are going to see what are DevOps, MLOps, and AIOps. We will try to get through their basics and then we will explore the differences between them. Let's start this article without wasting any time:-
What is DevOps?
DevOps: Integration of development, operations, and quality assurance
DevOps collaborate across development, quality assurance, and operations involving people, processes, and technology to optimize software development and release throughput using continuous integration (CI) and continuous delivery (CD) cycles.
Every software company consists of two teams: one is the development team, which designs and develops the system from the ground up, and the other is the operations team, which tests and deploys the developed product. In most cases, the operations team provides information about bugs and other issues while the development team is working on them. It is possible that the development team moved to a new project while the operations team contributed to the previous project. This pushes back deadlines and slows down the entire software development cycle.
What if the development team and operations team work together and the barrier of misunderstanding is broken? That is what the DevOps concept is all about. DevOps has become a popular term used in the IT world to describe the process of software development, operations, and management within a single system. It's not just a tool or method that can get the job done; Rather, it is an approach that allows for greater flexibility as operations change. DevOps is basically about collaboration between developers and manual and automated systems.
7Cs of the DevOps lifecycle:
Ongoing development
Continuous integration
Continuous testing
Continuous use
Continuous feedback
Continuous monitoring
continuous operation
What is MLOps?
MLOps LifeCycle
MLOps is an ML engineering culture and practice to bring together ML system development (Dev) and ML system operations (Ops) where data scientists, data engineers, and operations teams work together.
The process of developing, implementing, and maintaining machine learning models is called machine learning operations. Machine learning, DevOps, and data engineering are combined in this field to find faster, easier, and more efficient ways for productive machine learning. It is a discipline devoted to the development, scalable and consistent implementation of algorithms in production.
Think of MLOps as DevOps for a machine learning pipeline. Data scientists, data engineers, and operations teams work together for it. If done right, it increases shared understanding among all teams about machine learning projects. Data science and data engineering teams can clearly benefit from MLOps. Sharing infrastructure increases openness as members of both teams sometimes works in silos.
MLO builds an ML pipeline to cover all stages of machine learning:
- data retrieval
- Research and Data Validation
- Data maintenance or data pre-processing
- Characteristics analysis
- Learning and assessment models
- Model validation
- implement the model or maintain the model and
- Model monitoring for data drift and concept drift
What is AIOps?
AIOps LifeCycle
The AI ​​Ops platform can ingest, index, and normalize events or telemetry from multiple domains, providers, or sources including existing infrastructure, network, application, cloud or monitoring tools.
According to Gartner, the company that first coined the term, AIOps is defined as a combination of big data and machine learning that automates IT operations, including event correlation, outlier detection, and causality. AIOps technology automatically pinpoints the root cause of IT events and provides best-in-class diagnostic data that enables technical teams to work to resolve issues with a focus on improving IT operational efficiency.
Five stages of AIOps adoption:
- collection
- accumulation
- analysis
- execution
- DevOps vs MLOps vs AIOps DevOps collaborate across development, quality assurance, and operations, spanning people, processes, and technology, to streamline software development cycles and reduce the average time for solutions
MLOps is a discipline that brings together machine learning, data engineering, and development to create automated ML channels for continuous learning and CI/CD to effectively manage the entire ML lifecycle
AIOps is a platform for monitoring and automating the flow of data and information from IT applications using big data, machine learning, and other advanced analytics technologies.
Conclusion:
We have successfully covered the main difference between DevOps, MLOps, and AIOps What are DevOps, MLOps, and AIOps?
You should also check out, Django Developer Roadmap, Python Developer Roadmap, C++ Complete Roadmap, Machine Learning Complete Roadmap, and Laravel Developer Roadmap.
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