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From Kubernetes to Kubeflow

  • Course Number

    Kubeflow
  • Self-Paced

What is this about?

Kubeflow is a specialized ML platform that is built for Kubernetes and runs in Kubernetes clusters as a collection of Pods and Operators. Kubeflow harnesses the power of Kubernetes to orchestrate containerized environments allowing enterprises to optimize the path from development to production. Kubeflow provides container images to run ML workloads and IDEs, such as JupyterLab Notebooks. Kubeflow is a Data Scientist obsessed platform that leverages the power of Kubernetes to really improve the Model Development Lifecycle by abstracting away the K8s complexity so Data Scientists can focus on data science. This workshop is for those who are interested in exploring the necessary Kubernetes components to support Kubeflow and how those components are extracted away for your benefit. The agenda is as follows:

  • Kubeflow Notebooks
  • Kubernetes and Kubeflow
  • Using Notebook Servers in Academy

Instructor Led Option

This course is available on a monthly basis with an instructor if you would prefer to take the course live. If this is your preference please navigate and sign up here .

What is Kubeflow?

Kubeflow as a project got its start over at Google. The idea was to create a simpler way to run TensorFlow jobs on Kubernetes. So, Kubeflow was created as a way to run TensorFlow, based on a pipeline called TensorFlow Extended and then ultimately extended to support multiple architectures and multiple clouds so it could be used as a framework to run entire machine learning pipelines. The Kubeflow open source project (licensed Apache 2.0) was formally announced at the end of 2017.

In a nutshell, Kubeflow is the machine learning toolkit that runs on top of Kubernetes. Kubeflow’s combined components allow both data scientists and DevOps to manage data, train models, tune and serve them, as well as monitor them.

For whom is the “Distributed Training” course?

Data scientists and DevOps with little or no experience with Kubeflow

About the Instructor

Instructor



Chase is a professional cloud native geek with a passion for connecting business problems to engineering solutions. Chase is currently investing his time in helping clients get the most out of their MLOps platforms (more specifically Kubeflow) by improving feedback loops and tightening up their model development life cycle.











Requirements

We assume that you have basic familiarity with cloud computing environments like AWS, GCP or Azure as well as a basic understanding of cloud-native architectures and Kubernetes concepts like pods, controllers, nodes, container images, volumes, etc. Additionally we assume that you have familiarity with ML concepts like algorithms, model training and parameter tuning

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