We can see that the prototype of a cloud-native operating system begins to take shape. This is a good era for developers because cloud infrastructure and cloud-native computing technologies have significantly improved the business innovation speed.
Given the rise of Kubernetes as an enterprise platform management system, it makes a lot of sense to have a way to manage our machine learning workloads in a similar manner. In the rest of this section we take a look at each of the component groups, some of their components, and how they are used within the Kubeflow platform.
Google Kubeflow, machine learning for Kubernetes, begins to take shape
MLOps is short for machine learning operations which takes into account the complete lifecycle and maintenance of a machine learning solution in a similar fashion as DevOps and DataOps do in their respective fields. The framework aims to deploy and maintain machine learning models in production with reliability and efficiency.
In recent years, AI and Machine Learning (ML) have seen tremendous growth across industries in various innovative use cases. It is the most important strategic trend for business leaders. When we dive into a technology, the first step is usually experimentation on a small scale and for very basic use cases, then the next step is to scale up operations. Sophisticated ML models help companies efficiently discover patterns, uncover anomalies, make predictions and decisions, and generate insights, and are increasingly becoming a key differentiator in the marketplace. Companies recognise the need to move from proof of concepts to engineered solutions, and to move ML models from development to production. There is a lack of consistency in tools and the development and deployment process is inefficient. As these technologies mature, we need operational discipline and sophisticated workflows to take advantage and operate at scale. This is popularly known as MLOps or ML CI/ CD or ML DevOps. In this article, we explore how this can be achieved with the Kubeflow project, which makes deploying machine learning workflows on Kubernetes simple, portable, and scalable. 2ff7e9595c
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