Why Kubernetes is a Great Choice for Data ScientistsSaikumar TalariBlockedUnblockFollowFollowingApr 23Today containers technologies are becoming more popular than others, developers are coming up with new approaches for writing and deploying applications.
With the help of containers, a developer can easily combine and integrate application with all the parts it requires, including libraries and dependencies, and export it all out as a single package, but without the overhead of a traditional virtual machine.
About KubernetesKubernetes is an open source system for handling clusters of containers.
To processes this, it offers tools for deploying applications, doing changes to existing container type applications, scaling those applications as required and helps in optimizing the use of the existing hardware below your containers.
It is designed to be fault-tolerant and extensible by allowing application components to start again and move across systems as required.
Important features of Kubernetes:Kubernetes is essential for software developers and systems operators as they love to deploy and manage various applications in Linux containers(LC).
LC’s provide the base for deployments and reproducible builds, but kubernetes and its community provide important features that make containers substantial for running real applications, like:Declarative deployments, that allow you to depend on kubernetes to establish your production environment in a staging environment.
Ubiquitous monitoring makes it simple to monitor the performance and different metrics regarding any component of a system and showcase them in a meaningful way.
Deployment and continuous integration, so you can shift from a Git commit to a passing test suite to a code running in production.
Flexible service routing, which means slowly rolling updates and scaling services out.
Kubernetes for data scienceMany data scientists have similar concerns that software engineers have: portable and reproducible environments; repeatable experiments; monitoring and tracking metrics in production; credential management; effortless scale-out and flexible routing.
It is not so difficult to see some of the analogies among things application developers do with Kubernetes and things data scientists would like do:Continuous batch jobs, like Continuous integration and development pipelines, are analogous to pipelines in machine learning, In that multiple coordinated stages which need to work simultaneously in a reproducible way to extract features; process data and test, train, and deploy models.
Microservice architectures allow easy debugging of machine learning models within the pipeline and aid alliance among data scientists and other team members.
Declarative configurations that that illustrates the connections between services facilitate creating models across platforms and reproducible learning pipelines.
Data scientists share many of the similar challenges as application developers do, but they have some distinct challenges related to how data scientists work and to the that models of machine learning can be hard to test and monitor than conventional services.
Many data scientists use interactive notebooks to do their exploratory work.
Notebook environments, which are developed by Project Jupyter, offers an interactive literate programming environment in which users can combine explanatory code and text; run and manipulate the code; and inspect its output.
Kubernetes for data scientistsData scientists may not show interest on kubernetes as a career option and it’s ok!.One of the major advantages is kubernetes is that it is a powerful framework capable of building higher-level tools.
Binder service is one such tool, it uses a Git repository from Jupyter notebooks, builds a container image to aid them, In the next step it launches the image in cluster of kubernetes with an exposed route so you can have an access to it via the public internet.
Kubernetes for machine learning in productionKubernetes has much to offer data scientists, interested in developing techniques to solve business problems with the help of machine learning.
But, it also has a lot to offer the teams who implement those techniques in production.
In some cases machine learning represents a different production workload to train models and provide insights.
Instead, machine learning is largely put into production as an important part of an intelligent application.
Kubeflow for Machine LearningKubeflow project is aimed at engineers in machine learning who need to stand up and maintain machine learning pipelines and workloads on kubernetes.
It is an excellent way to run frameworks like JupyterHub, TensorFlow, PyTorch and Seldon under kubernetes and hence represents a path to real portable workloads: a machine learning engineer or a data scientist can develop a pipeline on a laptop and can deploy it anywhere.