An Ultimate Guide to Migrating Your Machine Learning to Kubeflow: 4 Steps Towards Excelling In Machine Learning Production

Introduction
When you sit down to think about it: architects experiment and build models in AutoCAD, Geophysicists experiment and build earth models in Schlumberger. But what do Data Scientists have?

I introduce to you the amazing Kubeflow! The platform’s state-of-the-art features, matched with its plethora of tools, means deploying all your machine learning workflows and models on a Data Science Cluster jam-packed with functionalities (such as reproducible experimentation and Kubernetes orchestration).

But that’s where the question pops: do you know how to migrate to Kubeflow? No? Well, worry, not! With this article, we aim to teach you exactly that. Curious to learn? Let’s dive right in!

But What Exactly Is Kubeflow?
Before we start talking about how you can easily move data science to Kubeflow, let’s make sure we know what Kubeflow is. So, let’s get started?

The first thing you should understand is that Kubeflow is a platform. The effort needed is high in Deploying the first product on Kubeflow, and it becomes easy as you deploy more products.

Kubeflow is an open-source, i.e., a free platform that allows accelerating the time to develop, run, and deploy machine learning experiments (‘runs and pipelines’ as per Kubeflow) on top of the Kubernetes cluster. Break down the word ‘Kubeflow’ into ‘Kube’ and ‘Flow’ and find the Kubernetes server orchestration and scheduler, that can work in any cloud.

What Do You Use Kubeflow For?
Kubeflow bridges the gap between Machine Learning Experiments and Machine Learning in Production. This open-source machine learning toolkit first came about in 2017 and is useful for an array of different things.

These include:

  • Building revolutionary machine learning models
  • Analyzing a model’s performance
  • Tuning hyper-parameters
  • Managing to compute power
  • Deploying models to production
  • Version different models

The best part? You don’t have to worry about installing, downloading, purchasing, and maintaining anything other than Kubeflow on any cloud provider!

How To Migrate To Kubeflow

Now that we’ve made sure all of us know what Kubeflow is and what it’s used for, let’s get to the main component of this article: Understanding the migration of data science workflows to a unified Kubeflow platform.

To ensure the order and right people get the right information, we’ve divided it into two sections. Let’s get started!

Kubeflow for Machine Learning Operations (MLOps)

Kubeflow simplifies the world of MLOPs by isolating and abstracting out complex data science details like overfitting, regularization, feature selection, model accuracies, and what not.

Kubeflow isolates K8S from ML Scientist and abstracts Machine Learning for MLOps

Are you wondering how you can accomplish all of that? Worry not; we’ve made it all easy down below. Keep scrolling!

Kubeflow For Machine Learning Engineers And Data Scientists

There’s nothing worse than not having the right application to run your experiment on. Lucky for you, Kubeflow doesn’t project that problem on you!

Instead of scrambling here and there for solutions, you can migrate to a comprehensive Kubeflow platform. Not sure how that works? Keep reading then!

Create More Kubeflow Pipelines!
Now that you’ve tested out your first pipeline, create new pipelines (‘pipelined components’), and new components. Improve your experiments by developing both data validation as well as model validation components. Utilize Kubeflow tracking as you try different pipeline configurations and input settings.

Always seeking opportunities and challenges to continue developing as a scientist and technical leader.

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