MLOps, ModelOps, AIOps, or XOps?
XOps is a general term for operationalization applied to AI systems. MLOps, ModelOps, and AIOps are all subsets of XOps. AIOps refers to the use of AI in IT operations. According to MLOps SIG, the term MLOps is defined as the extension of the DevOps methodology to include Machine Learning and Data Science assets as first-class citizens within the DevOps ecology.
MLOps, like DevOps, emerges from the understanding that separating the ML model development from the process that delivers it — ML operations — lowers quality, transparency, and agility of the whole intelligent software.
There’s a fundamental difference between building a ML model in the Jupyter notebook model by a data scientist and deploying an ML model into a production system that generates business value. Although AI budgets are on the rise, only 22 percent of companies that use machine learning have successfully deployed an ML model into production.
MLOps aims to unify the release cycle for machine learning and software application release.
MLOps enables automated testing of machine learning artifacts (e.g. data validation, ML model testing, and ML model integration testing)
MLOps enables the application of agile principles to machine learning projects.
MLOps enables supporting machine learning models and datasets to build these models as first-class citizens within CI/CD systems.
MLOps reduces technical debt across machine learning models
MLOps help to achieve faster model development
MLOps enables vast scalability and management where thousands of models can be overseen, controlled, managed, and monitored for continuous integration, continuous delivery, and continuous deployment
MLOps provides reproducibility of ML pipelines, enabling more tightly-coupled collaboration across data teams, reducing conflict with devops and IT, and accelerating release velocity
MLOps enables greater transparency and faster response to drift-check, regulatory scrutiny and ensures greater compliance with an organization’s or industry’s policies.
production-ready machine learning models require a lot of iteration. Therefore, we must design the MLOps system by considering iteration as a critical factor. As we create a machine learning model, we need to think about how easy it is to add, remove or combine features of the dataset. Also, we may need to design with the ability to run two or three copies of model training in parallel. It is critical to develop the system so that it is possible to create a fresh copy of the pipeline itself.
Iteratively transform, aggregate, and de-duplicate data to create refined features. Most importantly, make the features visible and shareable across data teams, leveraging a feature store.
In machine learning, reproducibility indicates that when an algorithm is run repeatedly using a given dataset, the output produced is the same.
is of enormous help when we are moving projects from development to production. Also, the debugging and fixing of errors can be achieved quickly with the help of reproducibility. Reproducibility not only helps in maintaining data consistency but also helps in making sure that the output produced is correct.
MLOps is a set of engineering practices specific to machine learning projects that borrow from the more widely-adopted DevOps principles in software engineering. While DevOps brings a rapid, continuously iterative approach to shipping applications, MLOps borrows the same principles to take machine learning models to production. In both cases, the outcome is higher software quality, faster patching and releases, and higher customer satisfaction.
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