Lastly, Kubernetes serves as an excellent framework to deploy models effectively. Such independent scaling enables enhanced accuracy as well. This categorization enables Machine Learning scientists to utilize storage space and eventually cut costs in relation to the business requirements and budget. Since Kubernetes is a distributed cloud environment, computations and storage can be made separate. Now, when it comes to training the data sets, one needs a framework that can withstand intensive computations. This way, Machine Learning scientists can also pinpoint the right data source and deploy algorithms in a stable and constant fashion. These libraries are essential to create different Machine Learning models for specific problem statements. The containers in Kubernetes allow Machine Learning scientists to packaging up libraries for a particular domain. Kubernetes is a good fit for all three categories. Overall, Machine Learning has three progress steps – exploration, training, and deployment. Why are Kubernetes and Docker a good fit for Machine Learning? This is where Kubernetes and Docker come into play.

To be able to do so successfully, the Machine Learning team requires a framework that can enable them to develop software by keeping business goals and objectives in mind. Optimized and highly automated Machine Learning is developed and developed with business and compliance requirements in mind. The idea here is to not only increase the automation in the production cycles but also improve their quality. Using this practice, data science and operations teams can deploy, maintain, and govern all software production cycles. MLOps is a practice wherein data scientists, especially the ones specializing in Machine Learning, collaborate and communicate with the Operations team of a company to manage Machine Learning software lifecycles to be put into production. MLOps is essentially a compound of two terms – Machine Learning and Operations.

Play framework on docker on kubernetes