Data science workflows on Kubernetes with Kubeflow pipelines: Part 2
This blog series is part of the joint collaboration between Canonical and Manceps. Visit our AI consulting and delivery services page to know more. Introduction Kubeflow Pipelines are a great way to build portable, scalable machine learning workflows. It is a part of the Kubeflow project that aims to reduce the complexity and time involved with training and deploying machine learning models at scale. For more on Kubeflow, read our Kubernetes for data science: meet Kubeflow post. In this blog series, we demystify Kubeflow pipelines and showcase this method to produce reusable and reproducible data science. 🚀 In Part 1, we covered WHY Kubeflow brings the right standardization to data science workflows. Now, let’s see HOW you can accomplish that with Kubeflow Pipelines. In Part 2 of this blog series, we’ll work on building your first Kubeflow Pipeline as you gain an understanding of how it’s used to deploy reusable and reproducible ML pipelines. 🚀 Now, it is time to get our hands dirty! 👨🏻🔬 Building your first Kubeflow pipeline In this experiment, we will make use of the fashion MNIST dataset and the Basic classification with Tensorflow example and turn it into a Kubeflow pipeline, so you can repeat the same process with any notebook or script you already have worked on. You can follow the process of migration into the pipeline on this Jupyter notebook. Ready? 🚀 Step 1: Deploy Kubeflow and access the dashboard If you haven’t had the opportunity to launch Kubeflow, that is ok! You can deploy Kubeflow easily using Microk8s by following the tutorial – Deploy Kubeflow on Ubuntu, Windows and MacOS. We recommend deploying Kubeflow on your workstation if you have a machine with 16GB of RAM or more. Otherwise, spin up a virtual machine with these resources (e.g. t2.xlarge EC2 instance) and…
READ MORE: https://ubuntu.com//blog/data-science-workflows-on-kubernetes-with-kubeflow-pipelines-part-2
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